The rapid adoption of AR/VR devices, particularly in the gaming and entertainment sectors, has raised significant privacy concerns due to the need to share sensor data with platform servers to deliver immersive virtual experiences. One prominent privacy risk is the use of online tracking techniques by adversaries to profile and monitor users. While regulations such as GDPR and CCPA mandate user consent for collecting tracking cookies, adversaries are exploring alternative methods for user identification and monitoring. One such method is device fingerprinting, which relies primarily on network or browser characteristics (e.g., IP address, browser statistics) or unique device identities. Prior research has also explored the use of motion sensor imperfections for device fingerprinting. However, these approaches based on network, browser, and sensor imperfections are not persistent and can be easily mitigated through various countermeasures.

Extended Reality (XR) headsets are increasingly serving as repositories for substantial volumes of sensitive data and gateways to web applications. This transition highlights the need for convenient and secure user authentication solutions. Traditional password/PIN-based schemes are ill-suited to the XR's gesture- and voice-based interfaces and are prone to shoulder-surfing attacks. Some recent XR systems incorporate two-factor authentication, but it requires additional operations on a second device (e.g., a smartphone or wearable). In this work, we introduce the first effortless and inbuilt XR user authentication system by leveraging the harmonics of vibrations excited by users' vital signs. The system is transparent to users (no efforts during enrollment and authentication) and requires no additional hardware. The key idea is that vital signs (i.e., breathing and heart beating) naturally generate low-frequency mechanical vibrations, causing human skull to vibrate and produces harmonic signals. When the harmonics pass the human head, they carry rich biometrics associated with the wearer's skull structure and soft tissues, which can be captured by the XR motion sensors. Instead of directly utilizing the vibrations, we extract more reliable biometrics from the ratios among different harmonic frequencies, which capture wearers' unique head and facial attenuation properties and are non-volatile when the periodicity and amplitude of vital signs fluctuate. We further design an adaptive filter to mitigate the body motion distortions in common XR interactions. By adopting advanced deep learning models with the attention mechanism, our system realizes effective and robust authentication across XR scenarios. Evaluations across 10 months, with 52 users and two popular XR headsets, show that our system can accurately authenticate users with over 95% true positive rates and rejects unauthorized users with over 98% true negative rates under various XR scenarios, with biometrics remaining consistent over long-term periods.

Blood pressure (BP) is one of the most essential biomarkers for human health, widely used to diagnose cardiovascular diseases [3] and assess mental states [2, 5]. It is considered Protected Health Information (PHI) under HIPAA, and access to it typically requires explicit user consent. In this work, we uncover a novel privacy breach in the metaverse usage: a user's private BP information can be covertly and continuously surveilled using the unrestricted in-built motion sensors present in commodity VR headsets.

The adoption of augmented reality/virtual reality (AR/VR) has dramatically risen over the past few years across various application sectors, including immersive gaming, social communication, education, and tourism. The emerging use of AR/VR headsets has also created an excellent opportunity to promote pervasive health monitoring service as most AR/VR devices are already equipped with enriched sensing paradigm and will interact with users for a long time. In this talk, we aim to explore innovative technologies that enable fine-grained and personalized health status monitoring (e.g. vital signs and user identities) leveraging facial vibrations captured by the in-built motion sensor testbed on commodity AR/VR headsets. On one hand, it provides real-time health information required in virtual healthcare applications. For instance, a doctor can continuously monitor a patient's vital signs during the tele-medicine session at home, which helps the doctor to realize timely and precise diagnoses [2]. On the other hand, as people are spending increasing time in cyberspace (e.g., Metaverse), exposure to virtual and immersive contents requires high concentration on users' mind. Such usage cases may significantly increase the visual and psychological burden and induce potential health issues (e.g., anxiety, hypertension, sleep disorders) [1, 3, 5].

Indistinguishability is a fundamental principle of cryptographic security, crucial for securing data transmitted between Internet of Things (IoT) devices. This principle ensures that an attacker cannot distinguish between the encrypted data, also known as ciphertext, and random data or the ciphertexts of the two messages encrypted with the same key. This research investigates the ability of machine learning (ML) in assessing indistinguishability property in encryption systems, with a focus on lightweight ciphers. As our first case study, we consider the SPECK32/64 and SIMON32/64 lightweight block ciphers, designed for IoT devices operating under significant energy constraints. In this research, we introduce MIND-Crypt1, a novel MLbased framework designed to assess the cryptographic indistinguishability of lightweight block ciphers, specifically the SPECK32/64 and SIMON32/64 encryption algorithm in CBC mode (Cipher Block Chaining), under Known Plaintext Attacks (KPA). Our approach involves training ML models using ciphertexts from two plaintext messages encrypted with same key to determine whether ML algorithms can identify meaningful cryptographic patterns or leakage. Our experiments show that modern ML techniques consistently achieve accuracy equivalent to random guessing, indicating that no statistically exploitable patterns exists in the ciphertexts generated by considered lightweight block ciphers. Furthermore, we demonstrate that in ML algorithms with all the possible combinations of the ciphertexts for given plaintext messages reflects memorization rather than generalization to unseen ciphertexts. Collectively, these findings suggest that existing block ciphers have secure cryptographic designs against ML-based indistinguishability assessments, reinforcing their security even under round-reduced conditions.1We refer to our attack framework as MIND-Crypt which stands for “Machine learning based framework for assessing INDistinguishability of Cryptographic Algorithms.”

This longitudinal study explores recurring usability challenges faced by students in a university-level cybersecurity course during their first use of Thunderbird for PGP (Pretty Good Privacy) email encryption. Despite being tech-savvy and securityaware, students encountered persistent issues such as public key import failures, unintuitive interfaces, and a lack of feedback on successful encryption. Our multifaceted analysis included sentiment classification via the Hugging Face transformer pipeline, Google Trends to assess global search behavior, and N -gram/word cloud visualizations of student support emails. We also examined correlations between Thunderbird version updates and support inquiries, revealing a strong relationship between software changes and usability friction. That even technically proficient users struggled highlights a critical concern: if Thunderbird’s PGP features hinder advanced users, general users are likely to face even greater barriers. These findings underscore the urgent need for user-centered improvements in Thunderbird’s PGP integration-particularly in UI clarity, cross-platform consistency, and feedback mechanisms.

Blood pressure (BP) is one of the most essential biomarkers for various diseases. It is considered protected health information under HIPAA and usually needs the user's consent for access. In this work, we uncover an insidious privacy breach in metaverse usage: private BP information can be covertly obtained from unrestricted motion sensors in virtual reality (VR) headsets. The insight is that the motion sensors can capture the subtle vibrations induced by the blood waves in the major arteries. Such vibrations are highly correlated with users' cardiac cycles and BP. As adversaries can continuously obtain motion sensor data from VR headsets without users' consent, they can derive and collect users' BP information in metaverse apps or websites, leading to more severe consequences, such as discrimination, exploitation, and targeted harassment. To demonstrate this severe privacy leakage in the meta-verse, we develop a practical attack, BPSniff, which can reconstruct fine-grained blood flow patterns and derive BP based on motion sensor data from users' VR headsets. BP-Sniff is the first practical attack revealing the BP leakage in the metaverse without using dedicated equipment. Unlike previous mobile sensing approaches that require user-specific calibration, BPSniff bypasses this constraint, enabling truly stealthy passive BP attacks at scale. Our attack first employs a variational autoencoder to reconstruct high-fidelity blood flow patterns from VR headset motion sensor data. We then develop an Adam-optimized long short-term memory (LSTM) regression model that leverages BP-related fiducial features from successive blood flow patterns to continuously estimate the user's BP. We evaluate BPSniff through extensive experiments and a longitudinal study of 8 weeks, involving 37 participants and two VR headset models. The results show that BPSniff can achieve low mean errors of 1.75 mmHg for systolic blood pressure (SBP) and 1.34 mmHg for diastolic blood pressure (DBP), which are comparable to commercial BP monitors and satisfy the standard (i.e., mean error ≤ 5.0 mmHg) specified by FDA's AAMI protocol.

Despite the increasing popularity of voice assistants such as Amazon Alexa, the security implications of dynamic skill content (content modifiable without resubmission) in voice assistant skills (voice-activated applications) remain largely unexplored. This paper presents the first large-scale analysis of Alexa's dynamic content ecosystem using D-Explorer, a ChatGPT powered chatbot. From a dataset of 10,407 skill interactions, we investigate: 1) the mechanisms of Alexa dynamic content, 2) the associated security risks, and 3) the prevalence of these risks in published skills. Our analysis reveals that 34% of skills contain dynamic content in interactions, 95% access external resources (increasing attack vectors), 7% of skill conversations exhibit problematic (potentially harmful or privacy-infringing) interactions related to dynamic content, and 90% of skills connect to a potentially vulnerable dynamic resource during interaction. These findings expose significant vulnerabilities, highlighting the critical need for stricter developer rules and security measures to prevent unpredictable, harmful, and privacy compromising interactions within the Alexa skill ecosystem.

In today's technology-driven world, web services have opened up new opportunities for blind and visually impaired people to interact independently. Securing interactions with these services is crucial; however, currently deployed methods of web authentication mainly concentrate on sighted users, overlooking the specific needs of the blind and visually impaired community. In this paper, we address this critical gap by investigating the security and accessibility aspects of these web authentication methods when adopted by blind and visually impaired users. We model web authentication for such users as screen reader assisted authentication and introduce an evaluation framework called Authentication Workflows Accessibility Review and Evaluation (AWARE). Using AWARE, we then systematically assessed popular PC-based and smartphone-based screen readers against different types of deployed web authentication methods, including variants of 2FA and passwordless schemes, to simulate real-world scenarios for blind and visually impaired individuals. We analyzed these screen reader assisted authentication interactions with authentication methods in three settings: using a terminal (PC) with screen readers, a combination of the terminal (PC) and smartphone with screen readers, and smartphones with integrated screen readers. The results of our study underscore significant weaknesses in all of our observed screen reader assisted authentication scenarios for real-life authentication methods. These weaknesses, encompassing specific accessibility issues caused by imprecise screen reader instructions, highlight vulnerability concerning observed scenarios for both real-world and research literature based attacks, including phishing, concurrency, fatigue, cross-service, and shoulder surfing.

While advertising has become commonplace in today's online interactions, there is a notable dearth of research investigating the extent to which browser fingerprinting is harnessed for user tracking and targeted advertising. Prior studies only measured whether fingerprinting-related scripts are being run on the websites but that in itself does not necessarily mean that fingerprinting is being used for the privacy-invasive purpose of online tracking because fingerprinting might be deployed for the defensive purposes of bot/fraud detection and user authentication. It is imperative to address the mounting concerns regarding the utilization of browser fingerprinting in the realm of online advertising.

The most commonly adopted password management technique is to store web account passwords on a password manager and lock them using a master password. However, current online password managers do not hide the account passwords or the master password from the password manager itself, which highlights their real-world vulnerability and lack of user confidence in the face of malicious insiders and outsiders that compromise the password management service especially given its online nature. We attempt to address this crucial vulnerability in the design of online password managers by proposing a cloud-based password manager that does not learn or store master passwords and account passwords. We introduce the protocol design and report on a full implementation of the system. Our implementation provides several security features, including enforcement of a unique and secure password per each service, robustness to online password guessing attacks against the password manager and the web service, robustness to password dictionary attacks upon compromise of the password manager and the web service, and security against phishing attacks. Furthermore, to assess users’ perceptions of the security and usability of our password manager, we conducted a lab-based study. The findings from the study suggest that our system is close to being practical for everyday use and is viewed by users as both usable and more secure/trustworthy.

Push-based two-factor authentication (2FA) methods, such as the ”Just-Confirm” approach, are popular due to their user-friendly design, requiring users to simply approve or deny a push notification on their mobile device. However, these methods are vulnerable to ”concurrency attacks,” where an attacker attempts to log in immediately after the legitimate user, causing multiple push notifications that may lead to users inadvertently approving fraudulent access. This vulnerability arises because the login notifications are not uniquely bound to individual login attempts. To address this issue, Push-Compare-and-Confirm 2FA method enhances security by associating each login notification with a unique code displayed on both the authentication terminal and the push notification. Users are required to match these codes before confirming access, thereby binding the notification to a specific login attempt. Recognizing the ubiquity of mobile devices in daily life, we conducted a comprehensive user study with 65 participants to evaluate the usability and security of Push-Compare-and-Confirm. The study considered two scenarios: one where the user’s second-factor device (phone) is physically separate from the authentication terminal (e.g., logging in on a PC and confirming on the phone), and another where the phone serves as both the authentication terminal and the second-factor device. Participants completed 24 login trials, including both benign and attack scenarios, with varying code lengths (four characters and six characters). Our results indicate that while Push-Compare-and-Confirm maintains high usability in benign scenarios, with True Positive Rates (TPR) exceeding 95%, it presents significant challenges in attack detection. Participants correctly identified only about 50% of fraudulent login attempts, indicating a substantial vulnerability remains. These findings suggest that although Push-Compare-and-Confirm enhances security over standard push-based 2FA methods, additional measures—such as more intuitive interface designs, clearer visual cues, and user education on the importance of code verification—are necessary to improve attack detection rates without compromising usability.

Two-factor authentication (2FA) systems aim to secure user accounts, provided that either the password or the second factor device remains uncompromised. However, in this research, we challenge this perception and analyze the security of FIDO2 hardware security keys, which are increasingly used in 2FA and passwordless systems. Specifically, we develop an attack framework, analyze the underlying protocols of FIDO2, and examine the associated OS-level security. Through practical demonstrations, we illustrate how adversaries can exploit this framework and OS-level security measures to execute our designed attack, known as FIDOLA (FIDO2 Deception Attack via Overlays exploiting Limited Display Authenticators).

In AR/VR devices, the voice interface, serving as one of the primary AR/VR control mechanisms, enables users to interact naturally using speeches (voice commands) for accessing data, controlling applications, and engaging in remote communication/meetings. Voice authentication can be adopted to protect against unauthorized speech inputs. However, existing voice authentication mechanisms are usually susceptible to voice spoofing attacks and are unreliable under the variations of phonetic content. In this work, we propose SAFARI, a spoofing-resistant and text-independent speech authentication system that can be seamlessly integrated into AR/VR voice interfaces. The key idea is to elicit phonetic-invariant biometrics from the facial muscle vibrations upon the headset. During speech production, a user's facial muscles are deformed for articulating phoneme sounds. The facial deformations associated with the phonemes are referred to as visemes. They carry rich biometrics of the wearer's muscles, tissue, and bones, which can propagate through the head and vibrate the headset. SAFARI aims to derive reliable facial biometrics from the viseme-associated facial vibrations captured by the AR/VR motion sensors. Particularly, it identifies the vibration data segments that contain rich viseme patterns (prominent visemes) less susceptible to phonetic variations. Based on the prominent visemes, SAFARI learns on the correlations among facial vibrations of different frequencies to extract biometric representations invariant to the phonetic context. The key advantages of SAFARI are that it is suitable for commodity AR/VR headsets (no additional sensors) and is resistant to voice spoofing attacks as the conductive property of the facial vibrations prevents biometric disclosure via the air media or the audio channel. To mitigate the impacts of body motions in AR/VR scenarios, we also design a generative diffusion model trained to reconstruct the viseme patterns from the data distorted by motion artifacts. We conduct extensive experiments with two representative AR/VR headsets and 35 users under various usage and attack settings. We demonstrate that SAFARI can achieve over 96% true positive rate on verifying legitimate users while successfully rejecting different kinds of spoofing attacks with over 97% true negative rates.

End-to-end encryption (E2EE) is a critical security feature in mobile messaging apps like Signal, WhatsApp, and Skype, protecting private conversations from unauthorized access, even by service providers. However, this security heavily relies on users participating in an authentication ceremony to verify encryption key fingerprints, thwarting potential man-in-the-middle (MitM) attacks. This authentication ceremony involves comparing QR codes or readable/exchangeable codes (e.g., numeric or hexadecimal) to ensure a match. Failure to match these codes due to human errors or click-through behavior makes users vulnerable to MitM attacks, as recent research highlights users' inability to perform this crucial comparison task in current E2EE applications. This paper presents an innovative automated approach integrated into the Signal application to simplify the authentication ceremony by automating the safety number comparison task. The new approach streamlines the comparison process by removing the manual burden from users. It prompts users to share the safety number through two out-of-band (OOB) channels: an SMS message and orally during a phone call with the other party. Simultaneously, the approach automatically performs a comparison between the shared safety number and the locally generated safety number on the recipient's phone. This automated comparison ensures accuracy, reduces human error, and enhances security by making it doubly difficult for attackers, as both channels must be compromised simultaneously. The approach demonstrates improved effectiveness, eliminating human errors and resisting MitM attacks, making it more robust than the current implementation in the Signal application. The results show a 0% false acceptance and 0% false rejection rate, significantly enhancing security and usability in this critical authentication process.

Browser fingerprinting is a tracking technique used to distinguish individual users. By leveraging unique fingerprint features or combining multiple ones, websites can not only identify users but also monitor their online activities. A specific aspect of browser fingerprinting, known as canvas fingerprinting, generates distinct values based on the characteristics of users' devices. This unique trait of canvas browser fingerprinting can be employed in challenge-response authentication, enabling user verification without requiring additional actions and potentially replacing the need for two-factor authentication. Furthermore, canvas fingerprinting can serve as an alternative to cookies, facilitating functionalities like the “Remember me” feature. This paper introduces an implementation of man-in-the-middle attack called “CRSlash” that targets prevalent challenge-response authentication methods, with a particular focus on canvas finger-printing based challenge-response authentication. In the case of CRSlash, an attacker only needs to obtain a challenge from the targeted device once. Subsequently, they can successfully navigate the authentication process. Our investigation reveals that existing challenge-response authentication methods relying on canvas fingerprinting are vulnerable to this attack. This vulnerability persists in both one-time authentication scenarios and continuous authentication setups. The outcomes of the attack demonstrate that prior canvas authentication methods are inadequate in countering this new threat. In response to this security concern, we propose a novel approach to canvas fingerprinting-based challenge-response authentication, which we call “CanvasDict.” In the CanvasDict process, the website creates a distinct authentication dictionary using the user's browser fingerprint during the registration phase. Later, during the login phase, the website selects random challenges from this dictionary for the authentication process. Through in-depth analysis, we ascertain the effectiveness of our approach in thwarting the aforementioned attack. Our evaluation encompasses two modes of CanvasDict, and the results underscore the success of CanvasDict in neutralizing the potential risks posed by the attack. This paper highlights the significance of browser fingerprinting, specifically focusing on canvas fingerprinting, as a means of user tracking and authentication. It sheds light on the vulnerabilities of existing challenge-response authentication methods and proposes an innovative solution to bolster security in the realm of canvas fingerprinting-based authentication.

Smartphones have become ubiquitous for a range of social, financial, and personal endeavors, as well as for accessing sensitive resources like confidential files from organizations. Nevertheless, this extensive usage has also made smartphones vulnerable to multiple security risks posed by malicious ad-versaries who intend to breach user accounts or steal personal information. Specifically, high-profile individuals or organizations are susceptible to becoming targets of targeted attacks. Previous research has identified various vulnerabilities that can compro-mise smartphones and access users' confidential information. A prominent example of such a vulnerability, known as the “An-droid Debug Bridge (ADB) vulnerability,” is widely recognized as it enables an attacker to remotely access and manipulate an Android smartphone and perform malicious activities. However, the existing body of literature lacks a comprehensive examination of the implications of this vulnerability on modern authentication systems, web-based password managers, and financial and e-commerce applications. In this paper, we shed light on this area and evaluated the security of multi-factor authentication systems, browser-based password managers, and popular financial and e-commerce applications. For this purpose, we introduce the BADAuth11Named after ”Bridge Android Debug for malicious Authentication” attack that exploits a set of ADB utilities. Our results reveal the susceptibility of secure authentication systems and browser-based password managers to a sophisticated one-time attack on a non-rooted device even with the latest Android version (Android 14.0). Furthermore, our research exposes the alarming ability of adversaries to access all passwords stored by browser-based password managers, thus paving the way for more severe attacks, including large-scale breaches within organizational settings. Additionally, our assessment underscores potential privacy and security risks for financial and e-commerce apps under BADAuth attacks, along with possible risk mitigation strategies.

Consumer-grade brain computer interface, i.e., EEG headsets are getting popular in our daily life activities. These devices are low-cost, light-weight in design and powerful enough to interact with a computing device effectively. In medical-grade use, EEG signal helps to detect different brain disease, e.g., sleep disorder, Epilepsy, Parkinson's disease, Alzheimer's disease. In consumer-grade use, EEG signal helps to communicate with the computing system in an error-free way. The high density brain imaging techniques and the easy integration features of EEG headsets introduce serious privacy attack to end-users. In this paper, we introduce Disease Detector, an eavesdropping attack which infers information about brain disease from EEG signal collected during daily life activities, such as stationary activities (e.g., desk work, idle sitting), light ambulatory activities (e.g., stairs up and down, walking), and intense ambulatory activities (e.g., jogging, running). In this attack, we utilize a low-cost and light-weight consumer-grade EEG headset to integrate with a smartphone/smartwatch/computer using Bluetooth connection and collect data passively without user intervention. We show that how an attacker can infer user's private health conditions (i.e., Epilepsy) from uncontrolled EEG signal and use it for unknown malicious purposes (e.g., targeted advertisement, trigger disease symptoms with flashing strobe lights, high frequency sounds etc.). We evaluate the attack with spectral analysis and machine learning. Our machine learning results show accuracy of 82 % on stationary activities, 94 % on light ambulatory activities and 83 % on intense ambulatory activities in identifying an epileptic patient from a healthy person. Our work shows that, it is indeed feasible for an attacker to learn about serious health condition by analyzing EEG signal collected from a low-end EEG headset. We believe our work serves to raise awareness to a potentially hard-to address threat arising from consumer-grade EEG headset and provides insights to security researchers to consider robust security measurements to protect users' private sensitive information (e.g., health condition).

Data protection regulations, such as GDPR and CCPA, require websites and embedded third-parties, especially advertisers, to seek user consent before they can collect and process user data. Only when the users opt in, should these entities collect, process, and share user data. Websites typically incorporate Consent Management Platforms (CMPs), such as OneTrust and CookieBot, to solicit and convey user consent to the embedded advertisers, with the expectation that the consent will be respected. However, neither the websites nor the regulators currently have any mechanism to audit advertisers' compliance with the user consent, i.e., to determine if advertisers indeed do not collect, process, and share user data when the user opts out. In this paper, we propose an auditing framework that leverages advertisers' bidding behavior to empirically assess the violations of data protection regulations. Using our framework, we conduct a measurement study to evaluate four of the most widely deployed CMPs, i.e., Didomi, Quantcast, OneTrust, and CookieBot, as well as advertiser-offered opt-out controls, i.e., National Advertising Initiative's opt-out, under GDPR and CCPA. Our results indicate that in many cases user data is unfortunately still being collected, processed, and shared even when users opt-out. We also find that some CMPs are better than the others at conveying user consent and that several ad platforms ignore user consent. Our results also indicate that advertiser-offered opt-out are equally ineffective at protecting user privacy.

Reducing the level of user effort involved in traditional two-factor authentication (TFA) constitutes an important research topic. An interesting representative approach, Sound-Proof , leverages ambient sounds to detect the proximity between the second-factor device (phone) and the login terminal (browser), and it eliminates the need for the user to transfer PIN codes. In this article, we identify a weakness of the Sound-Proof system that makes it completely vulnerable to passive “environment guessing” and active “environment manipulating” remote attackers and proximity attackers. Addressing these security issues, we propose Listening-Watch , a new TFA mechanism based on a wearable device (watch/bracelet) and active browser-generated random speech sounds. As the user attempts to log in, the browser populates a short random code encoded into speech, and the login succeeds if the watch’s audio recording contains this code (decoded using speech recognition ) and is similar enough to the browser’s audio recording. The remote attacker, who has guessed/manipulated the user’s environment, will be defeated, since authentication success relies upon the presence of the random code in watch’s recordings. The proximity attacker will also be defeated unless it is extremely close (<50 cm) to the watch, since the wearable microphones are usually designed to capture only nearby sounds (e.g., voice commands).

The market size of augmented reality and virtual reality (AR/VR) has been expanding rapidly in recent years, with the use of face-mounted headsets extending beyond gaming to various application sectors, such as education, healthcare, and the military. Despite the rapid growth, the understanding of information leakage through sensor-rich headsets remains in its infancy. Some of the headset's built-in sensors do not require users' permission to access, and any apps and websites can acquire their readings. While theseunrestricted sensors are generally considered free of privacy risks, we find that an adversary could uncover private information by scrutinizing sensor readings, making existing AR/VR apps and websites potential eavesdroppers. In this work, we investigate a novel, unobtrusive privacy attack called FaceReader, which reconstructs high-quality vital sign signals (breathing and heartbeat patterns) based on unrestricted AR/VR motion sensors. FaceReader is built on the key insight that the headset is closely mounted on the user's face, allowing the motion sensors to detect subtle facial vibrations produced by users' breathing and heartbeats. Based on the reconstructed vital signs, we further investigate three more advanced attacks, including gender recognition, user re-identification, and body fat ratio estimation. Such attacks pose severe privacy concerns, as an adversary may obtain users' sensitive demographic/physiological traits and potentially uncover their real-world identities. Compared to prior privacy attacks relying on speeches and activities, FaceReader targets spontaneous breathing and heartbeat activities that are naturally produced by the human body and are unobtrusive to victims. In particular, we design an adaptive filter to dynamically mitigate the impacts of body motions. We further employ advanced deep-learning techniques to reconstruct vital sign signals, achieving signal qualities comparable to those of dedicated medical instruments, as well as deriving sensitive gender, identity, and body fat information. We conduct extensive experiments involving 35 users on three types of mainstream AR/VR headsets across 3 months. The results reveal that FaceReader can reconstruct vital signs with low mean errors and accurately detect gender (over 93.33%). The attack can also link/re-identify users across different apps, websites, and longitudinal sessions with over 97.83% accuracy. Furthermore, we present the first successful attempt at revealing body fat information from motion sensor data, achieving a remarkably low estimation error of 4.43%.

Emotional state leakage attracts increasing concerns as it reveals rich sensitive information, such as intent, demo graphic, personality, and health information. Existing emotion recognition techniques rely on vision and audio data, which have limited threat due to the requirements of accessing restricted sensors (e.g., cameras and microphones). In this work, we first investigate the feasibility of detecting the emotional state of people in the vibration domain via zero-permission motion sensors. We find that when voice is being played through a smartphone's loudspeaker or ear speaker, it generates vibration signals on the smartphone surface, which encodes rich emotional information. As the smartphone is the go-to device for almost everyone nowadays, our attack based only on motion sensors raises severe concerns about emotion state leakage. We comprehensively study the relationship between vibration data and human emotion based on several publicly available emotion datasets (e.g., SAVEE, TESS). Time-frequency features and machine learning techniques are developed to determine the emotion of the victim based on speech vibrations. We evaluate our attack on both the ear speakers and loudspeakers on a diverse set of smartphones. The results demonstrate our attack can achieve a high accuracy, with around 95.3% (random guess 14.3%) accuracy for the loudspeaker setting and 60.52% (random guess 14.3%) accuracy for the ear speaker setting.

Notification-based authentication is an emerging Two-Factor Authentication (2FA) and passwordless solution that leverages interactive notifications on mobile devices to establish an additional layer of security beyond passwords. This method has gained popularity due to its convenience and ease of deployment in organizational settings. In this work, we aim to evaluate the effectiveness of notification-based authentication systems when a malicious entity is present on the user's computer, such as a keylogger or malicious extension, without compromising the mobile devices or communication channels. Furthermore, we investigate how the lack of information provided to users during the authentication workflow can lead to the approval of malicious authentication requests. Notably, we highlight the vulnerability of cross-service attacks, where an attacker authenticates to Service B while the user is attempting to authenticate to Service A. Our proof-of-concept attack program demonstrates the susceptibility of various notification-based authentication systems, and our user study reveals an alarming 82.2% cross-service attack success rate. These findings suggest a potential vulnerability in notification-based authentication systems, where the attacker compromise user account without compromising possession-factor device, such as smartphones.

Despite the rapid growth of augmented reality and virtual reality (AR/VR) in various applications, the understanding of information leakage through sensor-rich headsets remains in its infancy. In this poster, we investigate an unobtrusive privacy attack, which exposes users' vital signs and embedded sensitive information (e.g., gender, identity, body fat ratio), based on unrestricted AR/VR motion sensors. The key insight is that the headset is closely mounted on the user's face, allowing the motion sensors to detect facial vibrations produced by users' breathing and heartbeats. Specifically, we employ deep-learning techniques to reconstruct vital signs, achieving signal qualities comparable to dedicated medical instruments, as well as deriving users' gender, identity, and body fat information. Experiments on three types of commodity AR/VR headsets reveal that our attack can successfully reconstruct high-quality vital signs, detect gender (accuracy over 93.33%), re-identify users (accuracy over 97.83%), and derive body fat ratio (error less than 4.43%).

Vital signs (e.g., breathing and heart rates) and personal identities are essential information for personalized medicine and healthcare. The popularity of augmented reality/virtual reality (AR/VR) provides an excellent opportunity for enabling long-term health monitoring in a broad range of scenarios, including virtual entertainment, education, and telemedicine. However, commercial-off-the-shelf AR/VR devices do not have dedicated biosensors for providing vital signs and personal identities. In this work, we propose a novel framework that can generate fine-grained vital sign signals and other personalized health information of an AR/VR user through passive sensing on AR/VR devices. In particular, we find that the user's minute facial vibrations induced by breathing and heart beating can impact the readily available motion sensors on AR/VR headsets, which encode rich vital sign patterns and unique biometrics. The proposed framework further estimates the breathing and heartbeat rates, detects the gender and identity, and derives the body fat percentage of the user. To mitigate the impacts of body movement, we design an adaptive filtering scheme to cancel the spontaneous and non-spontaneous motion artifacts. We also develop unique facial vibration features and deep learning techniques to facilitate vital sign signal reconstruction and user identification. Extensive experiments demonstrate that our framework can achieve a low error of vital sign signal reconstruction and rate measurement, along with 95.51% and 93.33% accuracy on identity and gender recognition.

Virtual Reality (VR) has gained popularity in numerous fields, including gaming, social interactions, shopping, and education. In this paper, we conduct a comprehensive study to assess the trustworthiness of the embedded sensors on VR, which embed various forms of sensitive data that may put users’ privacy at risk. We find that accessing most on-board sensors (e.g., motion, position, and button sensors) on VR SDKs/APIs, such as OpenVR, Oculus Platform, and WebXR, requires no security permission, exposing a huge attack surface for an adversary to steal the user’s privacy. We validate this vulnerability through developing malware programs and malicious websites and specifically explore to what extent it exposes the user’s information in the context of keystroke snooping. To examine its actual threat in practice, the adversary in the considered attack model doesn’t possess any labeled data from the user nor knowledge about the user’s VR settings. Extensive experiments, involving two mainstream VR systems and four keyboards with different typing mechanisms, demonstrate that our proof-of-concept attack can recognize the user’s virtual typing with over 89.7% accuracy. The attack can recover the user’s passwords with up to 84.9% recognition accuracy if three attempts are allowed and achieve an average of 87.1% word recognition rate for paragraph inference. We hope this study will help the community gain awareness of the vulnerability in the sensor management of current VR systems and provide insights to facilitate the future design of more comprehensive and restricted sensor access control mechanisms.

Instant-messaging (IM) and voice over IP (VoIP) applications like WhatsApp, Zoom, and Skype have made people extremely reliant on online communications for their audio, video, and text conversations. Since more people are using these platforms to talk to each other and share sensitive information, many ongoing concerns have been raised about how the government and law enforcement monitor these platforms. Due to these concerns, the need for a method to secure confidential messages and electronic conversations has grown. This solution could be achieved by implementing an end-to-end encryption (E2EE) system without relying on any first or third parties, such as an online service or a centralized infrastructure like a public key infrastructure (PKI), which may be attacked, malicious, or coerced by law enforcement and government surveillance programs. In this systematization of knowledge paper, we first introduce the most popular E2EE apps, including their underlying E2EE messaging protocols. Then, based on the existing research literature, we investigate and systematize their E2EE features, including their underlying authentication ceremonies. Even though many research studies have examined some messaging services, we analyze and evaluate a broader set of the most popular E2EE apps and their underlying authentication ceremonies. Based on our evaluation, we have determined that all current E2EE apps, particularly when operating in opportunistic E2EE mode, are incapable of repelling active man-in-the-middle (MitM) attacks. In addition, we find that none of the current E2EE apps provide better and more usable authentication ceremonies, resulting in insecure E2EE communications against active MitM attacks. The conclusions of this systematization paper could influence future research in the field, including any improvements to the implementation of E2EE systems and authentication ceremonies that provide powerful protections against eavesdropping and MitM attacks.

Malware-infected terminals pose a pervasive threat to authentication systems. As password-only authentication cannot adequately protect against malware on terminals, the literature proposes several authentication methods claiming to provide security in the presence of significant security threats, including infected terminals. Most methods incorporate a password-independent factor in the authentication process to mitigate these threats. According to the community view in the literature, 2FA-oriented methods appear to be secure in the presence of malware on the authentication terminal. In this work, we systematize these 2FA-based academic schemes' threat models and authentication procedures to examine how they ensure security at every step of the authentication process. Additionally, we present an active concurrent attack framework named CSI(Concurrent Session Injection) and have done a comprehensive analysis of studied academic authentication systems against it. Furthermore, we systematize secure authentication systems from the literature that claim to provide protection against user terminal malware and concurrent attacks and point out their potential vulnerabilities. Our research emphasizes the significance of taking proper security measures against such threats and creates the opportunity to design more secure authentication systems in future research.

The growing adoption of voice-enabled devices (e.g., smart speakers), particularly in smart home environments, has introduced many security vulnerabilities that pose significant threats to users' privacy and safety. When multiple devices are connected to a voice assistant, an attacker can cause serious damage if they can gain control of these devices. We ask where and how can an attacker issue clean voice commands stealthily across a physical barrier, and perform the first academic measurement study of this nature on the command injection attack. We present the BarrierBypass attack that can be launched against three different barrier-based scenarios termed across-door, across-window, and across-wall. We conduct a broad set of experiments to observe the command injection attack success rates for multiple speaker samples (TTS and live human recorded) at different command audio volumes (65, 75, 85 dB), and smart speaker locations (0.1-4.0m from barrier). Against Amazon Echo Dot 2, BarrierBypass is able to achieve 100% wake word and command injection success for the across-wall and across-window attacks, and for the across-door attack (up to 2 meters). At 4 meters for the across-door attack, BarrierBypass can achieve 90% and 80% injection accuracy for the wake word and command, respectively. Against Google Home mini BarrierBypass is able to achieve 100% wake word injection accuracy for all attack scenarios. For command injection BarrierBypass can achieve 100% accuracy for all the three barrier settings (up to 2 meters). For the across-door attack at 4 meters, BarrierBypass can achieve 80% command injection accuracy. Further, our demonstration using drones yielded high command injection success, up to 100%. Overall, our results demonstrate the potentially devastating nature of this vulnerability to control a user's device from outside of the device's physical space, and its limitations, without the need for complex and error-prone command injection.

Sound waves from speech can potentially induce vibrations, proportional to the speech signal, on nearby objects. Each of these objects introduces the risk for a malicious attacker to exploit the induced vibrations to eavesdrop on the speech. Such an eavesdropping attack is critical when we consider the potential for induced vibrations in standard magnetic hard disk drives (HDDs). As an instance of this threat, prior research has demonstrated that speech in certain scenarios can induce vibrations on the read/write head of an HDD in order to eavesdrop on the speech (Kwong et al.; Oakland'19). In this paper, we revisit this line of research and aim to provide a closer investigation into whether HDDs can in fact be used as a source for eavesdropping on speech vibrations. As a foundation for our study, we utilize an effective, and robust methodology using laser vibrometry to measure the subtle speech vibrations induced on the read/write head. The prior study tested only a single HDD and only machine-rendered speech in a single setting with very loud speech. Our work broadens the scope of this research in many significant ways. First, we test multiple popular HDDs of different models and sizes to evaluate the generalizability of the overall threat. Second, we evaluate the threat from live human speech spoken near an HDD, expanding the scope of the attack to include most real-world speech settings involving normal human conversations. Third, we define machine-rendered speech scenarios to explore different propagation media and degrees of speech loudness. Our findings are two-fold. First, we observed that live human speech traveling through the air is not generally strong enough to impact HDDs such that intelligible speech information is leaked. Second, most tested HDDs did not seem capable of eavesdropping on machine-rendered speech unless the speech is loud enough, or the HDD shares a surface or is in direct contact with the speaker device. This implies HDDs cannot eavesdrop live human speech.

Human speech signals produce sound waves that induce vibrations on objects that they encounter. Such vibrations can be measured via laser vibrometers and possibly used in speech eavesdropping attacks. However, there is still much to learn about when this attack is feasible. In this paper, we aim to broaden our understanding of the viability of laser eavesdropping attacks to compromise speech in the commercial user space. In our study, we design experiments to measure the subtle vibrations induced on commonly-available objects by nearby speech, using commercially sold, high-precision laser vibrometers. To observe idealized success rates of the attack, we maintain certain physical parameters in favorable conditions that represent best case scenarios for an attacker. We test three primary attack scenarios considering different relative positions to the target object. Additionally, we consider many important experimental parameters to understand the generalizability of the attack, including: speech sources, loudness levels, vibration propagation media, and object materials. Our vibrometer recorded signals were analyzed via a two-pronged methodology including, (1) time domain, frequency spectrum, cross correlation, and speech intelligibility metric analyses and (2) an information extraction analysis using both human listeners and automated recognition tools. Our results suggest that eavesdropping attacks using a laser vibrometer may be practical in some situations and parameter settings (i.e., intelligence missions). However, we find that live aerial human speech and machine-rendered speech at a normal conversational loudness level does not show signs of significant leakage in our analysis.

The open nature of voice input makes voice assistant (VA) systems vulnerable to various acoustic attacks (e.g., replay and voice synthesis attacks). A simple yet effective way for adversaries to launch these attacks is to hide behind barriers (e.g., a wall, a window, or a door) and give unauthorized voice commands without being observed by legitimate users. In this work, we develop an automated, training-free defense system that can protect VA systems from such thru-barrier acoustic attacks. Our study finds that acoustic signals passing through the barriers generally present a unique frequency-selective effect in the vibration domain. Thus, we propose to devise a system to capture this unique effect of barriers by leveraging low-cost, cross-domain sensing available in users’ wearables. The system replays the audio-domain signals with the wearable’s speaker and captures the conductive vibrations caused by the audio sounds in the vibration domain via the built-in accelerometer. To improve the proposed system’s reliability, we develop a unique vibration-domain enhancement method to extract the phonemes most sensitive to the frequency-selective effect of barriers. We identify effective vibration-domain features that capture the barriers’ effects in the vibration domain. A 2D-correlation-based method is developed to examine the speech similarity between the recordings from the VA system and the user’s wearable and detect thru-barrier attacks. Extensive experiments with various barriers and environments demonstrate that the proposed defense system can effectively defend random, replay, synthesis, and hidden voice attacks with less than 4% equal error rates.

The Augmented reality/Virtual reality (AR/VR) industry has ushered in a period of rapid development. The next decade leaves a massive imagination for AR/VR in terms of end product form, software, content, applications, and user increment. The AR & VR technology offers a gazillion of possibilities for smart healthcare. In this poster, we develop an innovative continuous blood pressure (CBP) estimation system leveraging the built-in motion sensors of AR/VR headsets for users. We design a deep learning-based PPG construction scheme using the motion sensor-based cardiac signal and estimate the continuous blood pressure using the regression model. Our experimental results show that our system can continuously estimate both systolic blood pressure (SBP) and diastolic blood pressure (DBP) with a mean error of less than 4 mmHg and 0.9 mmHg respectively within a day.

Augmented reality/virtual reality (AR/VR) headsets have attracted millions of users and gained predictable popularity. However, long-period usage of immersive technology may lead to health issues (e.g., cybersickness, anxiety). In this poster, we design a low-cost and personalized healthcare monitoring system grounded on vital sign tracking (i.e., breathing and heartbeat rate tracking), by exploiting built-in AR/VR motion sensors. The key insight is that the conductive vibrations induced by chest and heart movements can propagate through the user's cranial bones, thereby vibrating the AR/VR headset mounted on the user's head. To realize this system, we design signal processing techniques to cancel the human motions and derive the periods of breathing and heartbeat through frequency-domain analyses. We further design a user identification scheme based on respiratory and cardiac biometrics, which works with vital sign monitoring to provide personalized healthcare recommendations. Our experiment shows that the proposed scheme can achieve less than 5.7% error rate on breathing/heartbeat rate estimation and 95% accuracy on user identification.

The spread of digital disinformation (aka "fake news") is arguably one of the most significant threats on the Internet today which can cause individual and societal harm of large scales. The susceptibility to fake news attacks hinges on whether or not Internet users perceive a fake news article/snippet to be legitimate (real) after reading it. In this paper, we attempt to garner an in-depth understanding of users’ susceptibility to text-centric fake news attacks via a neuro-cognitive methodology (thus corroborating as well as extending the traditional behavioral-only approach in significant ways). In particular, we investigate the neural underpinnings relevant to fake vs. real news through EEG, a well-established brainimaging technique. We design and run an EEG experiment with human users to pursue a thorough investigation of users’ perception and cognitive processing of fake vs. real news. We analyze the neural activity associated with the fake vs. real news detection task for different categories of news articles.Our results show that there may be no statistically significant or automatically inferable differences in the way the human brain processes the fake vs. real news, while marked differences are observed when people are subject to (real or fake) news vs. resting state and even between some different categories of fake news. This neurocognitive finding may help to justify users’ susceptibility to fake news attacks, as also confirmed from the behavioral analysis. In other words, the fake news articles may seem almost indistinguishable from the real news articles in both behavioral and neural domains. Our work serves to dissect the fundamental neural phenomena underlying fake news attacks and explains users’ susceptibility to these attacks through the limits of human biology. We believe that this could be a notable insight for the researchers and practitioners suggesting that the human detection of fake news might be ineffective, which may also have an adverse impact on the design of automated detection approaches that crucially rely upon human labeling of text articles for building training models.

Mental health is an extremely important subject, especially in these unprecedented times of the COVID-19 pandemic. Ubiquitous mobile phones can equip users to supplement psychiatric treatment and manage their mental health. Mobile Mental Health (MMH) apps emerge as an effective alternative to assist with a broad range of psychological disorders filling the much-needed patient-provider accessibility gap. However, it also raises significant concerns with sensitive information leakage. The absence of a transparent privacy policy and lack of user awareness may pose a significant threat to undermining the applicability of such tools. We conducted a multifold study of - 1) Privacy policies (Manually and with Polisis, an automated framework to evaluate privacy policies); 2) App permissions; 3) Static Analysis for inherent security issues; 4) Dynamic Analysis for threat surface and vulnerabilities detection, and 5) Traffic Analysis.Our results indicate that apps’ exploitable flaws, dangerous permissions, and insecure data handling pose a potential threat to the users’ privacy and security. The Dynamic analysis identified 145 vulnerabilities in 20 top-rated MMH apps where attackers and malicious apps can access sensitive information. 45% of MMH apps use a unique identifier, Hardware Id, which can link a unique id to a particular user and probe users’ mental health. Traffic analysis shows that sensitive mental health data can be leaked through insecure data transmission. MMH apps need better scrutiny and regulation for more widespread usage to meet the increasing need for mental health care without being intrusive to the already vulnerable population.

Head-worn wearables, such as consumer-grade EEG headsets deployed in Brain Computer Interfaces (BCI), are getting popularity in the gaming and entertainment industry, and for people with certain disabilities. However, the increasing popularity of these wearables creates a significant privacy risk. For instance, tech companies are intending to use brainwave devices to detect workers' emotional state and mental condition. There are AI techniques that can learn what people are looking at in real-time. Silently conversing with the computing system is now possible using neuromuscular signals, for instance, untold digit recognition with higher accuracy is possible, which can retrieve untold PIN or password. These applications can reveal more private information than designated benign purpose, such as, while detecting performance of worker, sensitive information like Parkinson's disease, substance abuse disorder, heart disease, can be revealed from brainwave. The consequences of these privacy leakages may be potentially devastating, such as tracking users for targeted advertisements and launching targeted attacks against users. In this paper, we analyze current devices, explore previously studied attacks, research efforts to extract information from brainwave and analyze and synthesize potential future attacks from the current deployment. This systematization will provide right direction towards ensuring privacy risk of BCI devices, which is a pre-requisite to building future defense mechanisms against the attacks.

Modern speech recognition systems that are widely deployed today still suffer from known gender and racial biases. In this work, we demonstrate the potential to exploit the existing biases in these systems to achieve a new attack goal. We consider the potential for command disruption by an attacker that can be conducted in a manner that allows for access and control of a victim's voice assistant device. We present a novel attack, BiasHacker, which crafts specialized chatter noise to exploit racial and gender biases in speech recognition systems for the purposes of command disruption. Our experimental results confirm both racial and gender bias that is still present in the speech recognition systems of two modern smart speaker devices. We also evaluated the effectiveness of three types of chatter noise (American English (AE)-Male, Nigerian-Female, Korean-Female) for disruption and demonstrate that the AE-Male chatter is consistently more successful. Comparing the average success rate of each chatter type, in scenarios where disruption was achieved, we find that when targeting the Google Home mini smart speaker, the AE-Male chatter noise increases average disruption success compared to the Nigerian-Female and Korean-Female chatter noises by 112% and 121%, respectively. Also, when targeting the Amazon Echo Dot 2 the AE-Male chatter noise increases average disruption success compared to the Nigerian-Female and Korean-Female chatter noises by 42% and 69%, respectively.

This work enables mobile user authentication via finger inputs on ubiquitous surfaces leveraging low-cost physical vibration. The system we proposed extends finger-input authentication beyond touch screens to any solid surface for IoT devices (e.g., smart access systems and IoT appliances). Unlike passcode or biometrics-based solutions, it integrates passcode, behavioral and physiological characteristics, and surface dependency together to provide a low-cost, tangible and enhanced security solution. The proposed system builds upon a touch sensing technique with vibration signals that can operate on surfaces constructed from a broad range of materials. New algorithms are developed to discriminate fine-grained finger inputs and supports three independent passcode secrets including PIN number, lock pattern, and simple gestures by extracting unique features in the frequency domain to capture both behavioral and physiological characteristics including contacting area, touching force, and etc. The system is implemented using a single pair of low-cost portable vibration motor and receiver that can be easily attached to any surface (e.g., a door panel, a stovetop or an appliance). Extensive experiments demonstrate that our system can authenticate users with high accuracy (e.g., more than 97 percent within two trials), low false positive rate (e.g., less 2 percent) and is robust to various types of attacks.

Protection Jamming Devices (PJD) are specialized tools designed to sit on top of virtual assistant (VA) smart speakers and hinder them from “hearing” nearby user speech. PJDs aim to protect you from eavesdropping attacks by injecting a jamming signal directly into the microphones of the smart speaker. However, current signal processing routines can be used to reduce noise and enhance speech contained in noisy audio samples. Therefore, we identify a potential vulnerability for speech eavesdropping via smart speaker recordings, even when a PJD is being used. If an attacker can gain access to or facilitate smart speaker recordings they may be able to compromise a user’s speech with successful noise cancellation. Specifically, we are interested in the potential for Gaussian white noise (GWN) to be an effective jamming signal for a PJD. To our knowledge, the effectiveness of white noise and PJDs to protect against eavesdropping attacks has yet to receive a systematic evaluation that includes physical experiments with an actual PJD implementation.

Recent advances in speaker verification and speech processing technology have seen voice authentication being adopted on a wide scale in commercial applications like online banking and customer care support and on devices such as smartphones and IoT voice assistant systems. However, it has been shown that the current voice authentication systems can be ineffective against voice synthesis attacks that mimic a user's voice to high precision. In this work, we suggest a paradigm shift from the traditional voice authentication systems operating in the audio domain but susceptible to speech synthesis attacks (in the same audio domain). We leverage a motion sensor's capability to pick up phonatory vibrations, that can help to uniquely identify a user via voice signatures in the vibration domain. The user's speech is played/echoed back by a device's speaker for a short duration (hence our method is termed EchoVib) and the resulting non-linear phonatory vibrations are picked up by the motion sensor for speaker recognition. The uniqueness of the device's speaker and its accelerometer results in a device-specific fingerprint in response to the echoed speech. The use of the vibration domain and its non-linear relationship with audio allows EchoVib to resist the state-of-the-art voice synthesis attacks, shown to be successful in the audio domain.

Second factor (2FA) or passwordless authentication based on notifications pushed to a user's personal device (e.g., a phone) that the user can simply approve (or deny) has become widely popular due to its convenience. In this paper, we show that the effortlessness of this approach gives rise to a fundamental design vulnerability. The vulnerability stems from the fact that the notification, as shown to the user, is not uniquely bound to the user's login session running through the browser, and thus if two notifications are sent around the same time (one for the user's session and one for an attacker's session), the user may not be able to distinguish between the two, likely ending up accepting the notification of the attacker's session.

Recent years have witnessed the rapid development of automatic speech recognition (ASR) systems, providing a practical voice-user interface for widely deployed smart devices. With the ever-growing deployment of such an interface, several voice-based attack schemes have been proposed towards current ASR systems to exploit certain vulnerabilities. Posing one of the more serious threats,hidden voice attack uses the human-machine perception gap to generate obfuscated/hidden voice commands that are unintelligible to human listeners but can be interpreted as commands by machines. However, due to the nature of hidden voice commands (i.e., normal and obfuscated samples exhibit a significant difference in their acoustic features), recent studies show that they can be easily detected and defended by a pre-trained classifier, thereby making it less threatening. In this paper, we validate that such a defense strategy can be circumvented with a more advanced type of hidden voice attack calledHVAC. Our proposed HVAC attack can easily bypass the existing learning-based defense classifiers while preserving all the essential characteristics of hidden voice attacks (i.e., unintelligible to humans and recognizable to machines). Specifically, we find that all classifier-based defenses build on top of classification models that are trained with acoustic features extracted from the entire audio of normal and obfuscated samples. However, only speech parts (i.e., human voice parts) of these samples contain the useful linguistic information needed for machine transcription. We thus propose a fusion-based method to combine the normal sample and corresponding obfuscated sample as a hybrid HVAC command, which can effectively cheat the defense classifiers. Moreover, to make the command more unintelligible to humans, we tune the speed and pitch of the sample and make it even more distorted in the time domain while ensuring it can still be recognized by machines. Extensive physical over-the-air experiments demonstrate the robustness and generalizability of our HVAC attack under different realistic attack scenarios. Results show that our HVAC commands can achieve an average 94.1% success rate of bypassing machine-learning-based defense approaches under various realistic settings.

In this paper, we highlight a fundamental vulnerability associated with the widely adopted “Just Tap” push-based authentication in the face of a concurrency attack, and propose the method REPLICATE, a redesign to counter this vulnerability. In the concurrency attack, the attacker launches the login session at the same time the user initiates a session, and the user may be fooled, with high likelihood, into accepting the push notification which corresponds to the attacker's session, thinking it is their own. The attack stems from the fact that the login notification is not explicitly mapped to the login session running on the browser in the Just Tap approach. REPLICATE attempts to address this fundamental flaw by having the user approve the login attempt by replicating the information presented on the browser session over to the login notification, such as by moving a key in a particular direction, choosing a particular shape, etc. We report on the design and a systematic usability study of REPLICATE. Even without being aware of the vulnerability, in general, participants placed multiple variants of REPLICATE in competition to the Just Tap and fairly above PIN-based authentication.

The use of wearable devices (e.g., smartwatches) in two factor authentication (2FA) is fast emerging, as wearables promise better usability compared to smartphones. Still, the current deployments of wearable 2FA have significant usability and security issues. Specifically, one-time PIN-based wearable 2FA (PIN-2FA) requires noticeable user effort to open the app and copy random PINs from the wearable to the login terminal's (desktop/laptop) browser. An alternative approach, based on one-tap approvals via push notifications (Tap-2FA), relies upon user decision making to thwart attacks and is prone to skip-through. Both approaches are also vulnerable to traditional phishing attacks. To address this security-usability tension, we introduce a fundamentally different design of wearable 2FA, called SG-2FA, involving wrist-movement “seamless gestures” captured near transparently by the second factor wearable device while the user types a very short special sequence on the browser during the login process. The typing of the special sequence creates a wrist gesture that when identified correctly uniquely associates the login attempt with the device's owner. The special sequence can be fixed (e.g., “${@}{\$}{@}{\$}$”), does not need to be a secret, and does not need to be memorized (could be simply displayed on the browser). This design improves usability over PIN-2FA since only this short sequence has to be typed as part of the login process (no interaction with or diversion of attention to the wearable and copying of random PINs is needed). It also greatly improves security compared to Tap-2FA since the attacker can not succeed in login unless the user's wrist is undergoing the exact same gesture at the exact same time. Moreover, the approach is phishing-resistant and privacy-preserving (unlike behavioral biometrics). Our results show that SG-2FA incurs only minimal errors in both benign and adversarial settings based on appropriate parameterizations.

Augmented reality/virtual reality (AR/VR) has extended beyond 3D immersive gaming to a broader array of applications, such as shopping, tourism, education. And recently there has been a large shift from handheld-controller dominated interactions to headset-dominated interactions via voice interfaces. In this work, we show a serious privacy risk of using voice interfaces while the user is wearing the face-mounted AR/VR devices. Specifically, we design an eavesdropping attack, Face-Mic, which leverages speech-associated subtle facial dynamics captured by zero-permission motion sensors in AR/VR headsets to infer highly sensitive information from live human speech, including speaker gender, identity, and speech content. Face-Mic is grounded on a key insight that AR/VR headsets are closely mounted on the user's face, allowing a potentially malicious app on the headset to capture underlying facial dynamics as the wearer speaks, including movements of facial muscles and bone-borne vibrations, which encode private biometrics and speech characteristics. To mitigate the impacts of body movements, we develop a signal source separation technique to identify and separate the speech-associated facial dynamics from other types of body movements. We further extract representative features with respect to the two types of facial dynamics. We successfully demonstrate the privacy leakage through AR/VR headsets by deriving the user's gender/identity and extracting speech information via the development of a deep learning-based framework. Extensive experiments using four mainstream VR headsets validate the generalizability, effectiveness, and high accuracy of Face-Mic.

In this paper, we build a speech privacy attack that exploits speech reverberations from a smartphone's inbuilt loudspeaker captured via a zero-permission motion sensor (accelerometer). We design our attack Spearphone, and demonstrate that speech reverberations from inbuilt loudspeakers, at an appropriate loudness, can impact the accelerometer, leaking sensitive information about the speech. In particular, we show that by exploiting the affected accelerometer readings and carefully selecting feature sets along with off-the-shelf machine learning techniques, Spearphone can perform gender classification (accuracy over 90%) and speaker identification (accuracy over 80%) for the audio/video playback on the smartphone for our recorded dataset. We use lightweight classifiers and an off-the-shelf machine learning tool so that the attacking effort is minimized, making our attack practical. Our results with testing the attack on a voice call and voice assistant response were also encouraging, showcasing the impact of the proposed attack. In addition, we perform speech recognition and speech reconstruction to extract more information about the eavesdropped speech to an extent. Our work brings to light a fundamental design vulnerability in many currently-deployed smartphones, which may put people's speech privacy at risk while using the smartphone in the loudspeaker mode during phone calls, media playback or voice assistant interactions.

Existing academic research on vibration-based speech attacks has introduced interesting and intellectually appealing threat vectors with proof-of-concept demonstrations in controlled environments. The attacks presented in these studies exploit different types of sensors such as MEMS motion sensors, laser-based sensors, and some other sensors (camera, position error signal, piezo-disc) to measure the vibrations induced on an object by nearby sensitive speech. Such sensors are commonly found on mobile devices like smartphones and tablets that can be exposed to sensitive speech, revealing the significance of this potential threat. These studies have amassed significant attention in news and media and introduced concern to people about the safety of their day-to-day speech and around their personal, wireless and IoT devices. However, we hypothesize that the controlled experiments in the prior research maintain critical parameter values that are favorable to attack success (deviating from the limiting settings in a real-world scenario) and produce results that suggest a greater real-life threat level than actually exists.

We present a secure two-factor authentication (TFA) scheme based on the user’s possession of a password and a crypto-capable device. Security is “end-to-end” in the sense that the attacker can attack all parts of the system, including all communication links and any subset of parties (servers, devices, client terminals), can learn users’ passwords, and perform active and passive attacks, online and offline. In all cases the scheme provides the highest attainable security bounds given the set of compromised components. Our solution builds a TFA scheme using any Device-enhanced Password-authenticated Key Exchange (PAKE), defined by Jarecki et al., and any Short Authenticated String (SAS) Message Authentication, defined by Vaudenay. We show an efficient instantiation of this modular construction, which utilizes any password-based client-server authentication method, with or without reliance on public-key infrastructure. The security of the proposed scheme is proven in a formal model that we formulate as an extension of the traditional PAKE model. We also report on a prototype implementation of our schemes, including TLS-based and PKI-free variants, as well as several instantiations of the SAS mechanism, all demonstrating the practicality of our approach. Finally, we present a usability study evaluating the viability of our protocol contrasted with the traditional PIN-based TFA approach in terms of efficiency, potential for errors, user experience, and security perception of the underlying manual process.1

Due to the open nature of voice input, voice assistant (VA) systems (e.g., Google Home and Amazon Alexa) are vulnerable to various security and privacy leakages (e.g., credit card numbers, passwords), especially when issuing critical user commands involving large purchases, critical calls, etc. Though the existing VA systems may employ voice features to identify users, they are still vulnerable to various acoustic-based attacks (e.g., impersonation, replay, and hidden command attacks). In this work, we propose a training-free voice authentication system, WearID, leveraging the cross-domain speech similarity between the audio domain and the vibration domain to provide enhanced security to the ever-growing deployment of VA systems. In particular, when a user gives a critical command, WearID exploits motion sensors on the user’s wearable device to capture the aerial speech in the vibration domain and verify it with the speech captured in the audio domain via the VA device’s microphone. Compared to existing approaches, our solution is low-effort and privacy-preserving, as it neither requires users’ active inputs (e.g., replying messages/calls) nor to store users’ privacy-sensitive voice samples for training. In addition, our solution exploits the distinct vibration sensing interface and its short sensing range to sound (e.g., 25cm) to verify voice commands. Examining the similarity of the two domains’ data is not trivial. The huge sampling rate gap (e.g., 8000Hz vs. 200Hz) between the audio and vibration domains makes it hard to compare the two domains’ data directly, and even tiny data noises could be magnified and cause authentication failures. To address the challenges, we investigate the complex relationship between the two sensing domains and develop a spectrogram-based algorithm to convert the microphone data into the lower-frequency “ motion sensor data” to facilitate cross-domain comparisons. We further develop a user authentication scheme to verify that the received voice command originates from the legitimate user based on the cross-domain speech similarity of the received voice commands. We report on extensive experiments to evaluate the WearID under various audible and inaudible attacks. The results show WearID can verify voice commands with 99.8% accuracy in the normal situation and detect 97.2% fake voice commands from various attacks, including impersonation/replay attacks and hidden voice/ultrasound attacks.

In this paper, we propose Voicefox1, a defense against the threat of automated voice synthesis attacks in machine-based and human-based speaker verification applications. Voicefox is based on a hitherto undiscovered potential of speech-to-text transcription, already built into these applications. Voicefox relies on the premise that while the synthesized samples might be falsely accepted by the speaker verification systems and human listeners, they cannot be transcribed as accurately as a natural human voice by transcribers. Voicefox is not a speaker verification system, but rather an independent module that can be integrated with any speaker verification system to enhance its security against voice synthesis attacks.

Several sensitive operations, such as financial transactions, email construction, configurations of safety-critical devices (e.g., medical devices or smart home systems), are often performed via web interfaces from a host machine, usually a desktop or laptop PC. It is typically easy to secure the communication link between the local host machine and the remote server, for example, via a standard cryptographic protocol (e.g., TLS). However, if the host machine itself is compromised with a trojan or malware, the malicious adversary can manipulate the user-provided input (e.g., money transfer information, email content and configuration data) that can lead to severe consequences, including financial loss, damage of reputation, security breach, and even put human lives in danger.

Software crashes are nearly impossible to avoid. The reported crashes often contain useful information assisting developers in finding the root cause of the crash. However, crash reports may carry sensitive and private information about the users and their systems, which may be used by an attacker who has compromised the crash reporting system to violate the user's privacy and security. Besides, a single bug may trigger loads of identical reports which excessively consumes system resources and overwhelms application developers.

The currently deployed web authentication model, involving only entry-point authentication of users, does not do anything to protect against account takeover attacks. Once the attacker has compromised the entry-point authentication method, such as by learning a user's password or even two-factor authentication credentials via widely exploited mechanisms such as phishing and password database breaches, or has hijacked a login session, he can fully access and abuse the user's account and associated services. To respond to this critical vulnerability, we introduce the notion of non-stop post-entry authentication, to be integrated with any entry-point authentication method, using which the web service can proactively authenticate the user throughout the login session invisibly in the background without explicit user involvement and without the need for storing user-specific templates (like in biometric systems) thereby preserving user privacy.

The most researched behavioral biometrics for mobile device authentication involves the use of touch gestures as the user enters a graphical pattern password (like the one used on Android) or otherwise interacts with the device. However, due to the inherent static nature of these schemes, they are vulnerable to impersonation attacks. In this paper, we investigate challenge-response mechanisms to address this security vulnerability underlying the traditional static biometric schemes. We study the performance, security, and usability of two schemes of such challenge-response interactive biometric authentication geared for mobile devices and contrast them to static graphical pattern based biometrics. The first scheme is based on random graphical patterns. The second scheme, recently introduced for PC class of devices (not mobile), is based on a simple cognitive game involving semantic interactive random challenges. Our results show that the accuracy of user identification with these approaches is similar to static pattern based biometric scheme. Finally, we argue that utilizing interactivity and randomization significantly enhance the security against impersonation attacks. As an independent result, our work demonstrates that the use of motion sensors available on mobile device serves to improve the identification accuracy of schemes that only use touch-based gestures (static and interactive).

Voice access technologies are widely adopted in mobile devices and voice assistant systems as a convenient way of user interaction. Recent studies have demonstrated a potentially serious vulnerability of the existing voice interfaces on these systems to "hidden voice commands". This attack uses synthetically rendered adversarial sounds embedded within a voice command to trick the speech recognition process into executing malicious commands, without being noticed by legitimate users.

In this paper, we present a thorough study of voice impersonation attacks that can compromise the security of voice authentication technology deployed in several popular, state-of-the-art Android and iOS apps. Our study is based on our formulated Sneakers attack system that comprises a variety of well-known as well as newly designed attacks: (1) recorded and replayed voice of the authorized user (replay attack); (2) reordered and played-back voice of the authorized user (reorder attack); and (3) synthesized voice generated - based on voice conversion techniques - using an unauthorized user's voice (standard conversion attack), or using a noise-free recording from a text-to-speech engine (TTS conversion attack). Taking Sneakers as a basis, we report on a carefully designed study to examine a variety of real-world voice authentication apps for their vulnerability against malicious authentication. Our study follows a two-phase methodology. In the preliminary phase, we analyze 8 popular mobile apps against standard simplistic attack setups. Our results show that, while the tested apps seem to resist the reorder attack and the standard conversion attack, they are highly vulnerable to the replay attack. In the main phase of the study, we comprehensively assess 5 of the above apps against more advanced newly designed attack setups. Like in the preliminary phase, the apps prove to be highly vulnerable to the replay attack. More seriously, the apps also turn out to be highly insecure against our advanced attack setups, i.e., the reorder attack with coordinated timing and the TTS conversion attack, yielding success rates of 82%-98%. These malicious authentication measurement results are highly pertinent in practice because, we demonstrate that the apps generally work well in the benign authentication scenario to reliably "accept" an authorized user and "reject" an unauthorized user. Our work shows that many standard attacks that prior work demonstrated to be effective against standalone voice authentication algorithms do not work against current voice authentication apps. Yet, our new attack designs could still compromise these apps. Overall, our work highlights a serious vulnerability of real-world voice authentication apps, which seems very challenging to mitigate at a fundamental level.

This paper explores the effectiveness of common sound masking solutions deployed for preserving speech privacy in workplace environment such as hospitals, financial institutions, lawyers offices, nursing homes and government buildings. With the increased awareness about personal privacy among the general population, we set out to examine the effectiveness of current speech privacy preserving tools. We seek to determine if the general approach used by the current masking mechanisms is adequate to provide the level of privacy desired from these solutions. In addition, we also seek to investigate preservation of speech privacy in the face of ubiquitous and less conspicuous devices like smartphones that possess the capability of sound recording with inbuilt noise cancellation technology. Our approach in this paper is to expose the vulnerability in sound masking technology in scenarios that require preserving privacy in personal spaces. We use human listeners to attack speech privacy under sound masking where we aim to identify spoken words eavesdropped under different scenarios. We also test currently available speech recognition tools to assess their performance at decoding speech in noisy environment. Our results indicate that pink noise, the commonly used technology to provide speech privacy for use in personal space, is ineffective against a dedicated eavesdropping adversary that uses commonplace devices such as smartphones to record the speech and noise reduction tools to counteract sound masking.

Brain Computer Interfaces (BCI) are rapidly gaining popularity in consumer market. It is therefore important to analyze the security and privacy threats these devices may introduce to their users. In this paper, we explore how malicious access to brainwave signals may surreptitiously reveal users' privacy-sensitive medical conditions and personal information, while they are browsing the web (or interacting with an app). At a conceptual level, we investigate the potential of brainwave signals, captured during a user's normal interactions with visual stimuli (e.g., images and audio-visuals) through a website or computer, in exposing whether the user is suffering from a given medical disorder (e.g., drug abuse or autism) and to which demographics group the user belongs (e.g., young vs. elderly or male vs. female). At an empirical level, as two representative case studies into such conceptual attacks, we present a concrete brainwave privacy attack, (Brain) Hemorrhage11In the context of our work, the term “Hemorrhage” is an attack against brainwave privacy. Brain Hemorrhage is a type of alcoholic cocktail, and hence the terminology is also intended to capture one of the case studies of our work on Alcohol Use Disorder., focusing on the leakage of Alcohol Usage Disorder (AUD) and users' age group. Hemorrhage is designed using machine learning techniques to identify the users suffering from AUD and age group by analyzing the seemingly innocuous brainwave signals leaked online in response to users' viewing of simple images or watching of videos. Based on the publicly available EEG datasets on AUD and aging, our study shows that Hemorrhage can predict the presence or absence of alcohol usage disorder with the precision of 96% and the presence or absence of aging condition with 94% accuracy. We also analyze, visualize and interpret the differences in the brainwave signals corresponding to AUD and aging, which serves to justify why our attack succeeds. While the use of neuroimaging devices to diagnose medical disorders in clinical settings is a common practice in the medical field, our study constitutes one of the first steps towards exploring the malicious use of brainwave devices in compromising people's health information privacy in an online setting (otherwise protected under the HIPAA law) as well as their age privacy. Given any website can have unfettered, permission-less access to the signals captured by the current BCI devices, we believe that our work raises a serious online health privacy and age privacy issues as these devices get widely deployed.

Crypto Phones are emerging apps aimed for end-to-end secure communications. To detect man-in-the-middle (MITM) attacks, traditional Crypto Phones rely upon end-users to verbally exchange and compare a short protocol fingerprint. This requirement is often found to be inconvenient by the users. Hence, most current apps do not mandate fingerprint validation, allowing the users to opt-out, completely disregarding security in favor of usability. Besides, speaking the fingerprints is not free of user errors, which may lead to rejection of benign sessions degrading the user experience. In this paper, we address these fundamental problems by introducing Stethoscope1, a new Crypto Phone model that removes the human user from the loop of fingerprint comparison by using text-to-speech and speech-to-text transformations. Stethoscope automatically performs two tasks on behalf of the user: (1) creating the fingerprint by incorporating a fingerprint speaking tool at the sender side, built on top of a limited-domain text-to-speech engine, and (2) decoding/comparing the fingerprint at the receiver side based on a robust speech-to-text engine. Like the traditional design, Stethoscope relies on the receiver to manually verify the sender's voice to detect sophisticated voice attacks. On the sender side, we design an automated fingerprint speaking tool based on a limited-domain text-to-speech system using reordering of words in a phonetically-distinct word dictionary previously spoken by the user. This tool asks the users to speak all the words in the fingerprint dictionary only once to train the system. On the receiver side, to decode the fingerprint, we design a robust speech-to-text transcription method. We evaluate the effect of automating the fingerprint creation, transfer, and comparison in the Stethoscope design against manual speaker verification with a user study. Our results show that Stethoscope provides a 0% false accept and 0% false reject rate for the fingerprint comparison, while offering a higher level of speaker verification performance compared to traditional Crypto Phones.

In this paper, we consider the problem of transparently authenticating a user to a local terminal (e.g., a desktop computer) as she approaches towards the terminal. Given its appealing usability, such zero-effort authentication has already been deployed in the real-world where a computer terminal or a vehicle can be unlocked by the mere proximity of an authentication token (e.g., a smartphone). However, existing systems based on a single authentication factor contains one major security weakness - unauthorized physical access to the token, e.g., during lunch-time or upon theft, allows the attacker to have unfettered access to the terminal. We introduce ZEMFA, a zero-effort multi-factor authentication system based on multiple authentication tokens and multi-modal behavioral biometrics. Specifically, ZEMFA utilizes two types of authentication tokens, a smartphone and a smartwatch (or a bracelet) and two types of gait patterns captured by these tokens, mid/lower body movements measured by the phone and wrist/arm movements captured by the watch. Since a user's walking or gait pattern is believed to be unique, only that user (no impostor) would be able to gain access to the terminal even when the impostor is given access to both of the authentication tokens. We present the design and implementation of ZEMFA. We demonstrate that ZEMFA offers a high degree of detection accuracy, based on multi-sensor and multi-device fusion. We also show that ZEMFA can resist active attacks that attempt to mimic a user's walking pattern, especially when multiple devices are used.

Based on the notion of “emergence”, Xu et al. (Usenix Security 2012; TDSC 2013) developed the first concrete instantiation of emerging-image moving-object (EIMO) CAPTCHAs using 2D hollow objects (codewords), shown to be usable and believed to be secure. In this paper, we highlight the hidden security weaknesses of such a 2D EIMO CAPTCHA design. A key vulnerability is that the camera projection on 2D objects is constant (unlike 3D objects), making it possible to reconstruct the underlying codewords by superimposing and aggregating the temporally scattered parts of the object extracted from consecutive frames. We design and implement an automated attack framework to defeat this design using image processing techniques, and show that its accuracy in recognizing moving codewords is up to 89.2 percent, under different parameterizations. Our framework can be broadly used to undermine the security of different instances of 2D EIMO CAPTCHAs (not just the current state-of-the-art by Xu et al.), given the generalized and robust back-end theories in our attack, namely the methods to locate a codeword, reduce noises and accumulate objects' contour information from consecutive frames corresponding to multiple time periods. As a countermeasure, we propose a fundamentally different design of EIMO CAPTCHAs based on pseudo 3D objects, and examine its security as well as usability. We argue that this design can resist our attack against 2D EIMO CAPTCHAs, although at the cost of reduced usability compared to the - now insecure - 2D EIMO CAPTCHAs.

Internet of Things (IoT) is embodied by smart network-enabled devices that utilize computing power, networking, and miniaturization to enable richer and improved user experience. Due to their interconnectedness, ubiquitous nature and low computational power, trustworthy and secure communication between IoT devices has become a security concern. To authenticate the devices, “pairing” may be secured by the use of an auxiliary channel such as audio, visual and vibrations for sharing the key or keying material between the IoT devices. In this paper, we evaluate the security of vibration channel, susceptible to an acoustic eavesdropper, that can capture audio leakage from the vibrations of the transmitting IoT device. We propose a noisy vibration scheme for cloaking vibration sounds during pairing against such attacks. The scheme only requires a speaker for emitting the masking sound during key transmission. We evaluate the scheme in proximity, co-located and remote settings with an eavesdropping attacker. We also study motion sensor exploits against this scheme and compliment it with additional measures to mask vibration effects on motion sensors. Our scheme is user transparent and requires only a speaker (that may already be present on the device), so it can be readily implemented in the IoT setting, smart wearables, and other commodity gadgets.

We introduce a novel approach to password management, called SPHINX, which remains secure even when the password manager itself has been compromised. In SPHINX, the information stored on the device is theoretically independent of the user's master password. Moreover, an attacker with full control of the device, even at the time the user interacts with it, learns nothing about the master password - the password is not entered into the device in plaintext form or in any other way that may leak information on it. Unlike existing managers, SPHINX produces strictly high-entropy passwords and makes it compulsory for the users to register these passwords with the web services, which defeats online guessing attacks and offline dictionary attack upon service compromise. We present the design, implementation and performance evaluation of SPHINX, offering prototype browser plugins, smartphone apps and transparent device-client communication. We further provide a comparative analytical evaluation of SPHINX with other password managers based on a formal framework consisting of security, usability, and deployability metrics.

Context-centric sensor-based proximity detection (or, contextual co-presence detection) is a promising approach to defend against relay attacks in many mobile authentication systems, especially against unattended terminals (such as cars parked in unmonitored parking lots, remote gas station pumps, or stolen laptops). Prior work demonstrated the effectiveness of a variety of contextual sensor modalities for this purpose, including audio-radio environment (ambient audio, Wi-Fi, Bluetooth, and GPS, and combinations thereof) and physical environment (temperature, humidity, gas, and altitude, and combinations thereof). In this paper, we present a systematic assessment of such co-presence detection in the presence of a strong, context-manipulating attacker against unattended terminals. First, we show that it is feasible to manipulate, consistently control, and stabilize the readings of different acoustic and physical environment sensors (and even multiple sensors simultaneously) using low-cost, off-the-shelf equipment. Specifically, we show that it is possible to control the temperature using a home-grade hair dryer, affect the gas readings using a smoking cigarette, impact the altitude/pressure with a simple air compressor, or relay audio signals recorded at one end to the other thereby causing both sides to perceive a very similar acoustic environment. Second, based on these capabilities and the strengthened threat model, we show that an attacker who can manipulate the context gains a significant advantage in defeating contextual co-presence detection. For systems that use multiple sensors, we investigate two sensor fusion approaches based on machine learning classification techniques-features-fusion and decisions-fusion, and show that both are vulnerable to context manipulation attacks but the latter approach can be more resistant in some cases. We further consider other defensive approaches that may be used to reduce the impact of even such a strong context-manipulating attacker. Our work represents the first concrete step towards analyzing, extending, and systematizing prior work on contextual co-presence detection under a stronger, but realistic adversarial model.

Social engineering is a well-established and well-studied threat especially against healthy computer users. Little studied, however, is the level of vulnerability to social engineering attacks against people with medical conditions. Social disorders in particular may make people more susceptible to such attacks. In this paper, as an initial line of investigation into this understudied research line, we launch a study of phishing, a prominent social engineering attack, against people suffering from autism spectrum disorder, a unique developmental disorder characterized by hampered social skills and communication.

Keyboard acoustic side channel attacks to date have been mostly studied in the context of an adversary eavesdropping on keystrokes by placing a listening device near the intended victim creating a local eavesdropping scenario. However, being in close physical proximity of the victim significantly limits the applicability of the attack.

According to recent research, motion sensors available on current smartphone platforms may be sensitive to speech signals. From a security and privacy perspective, this raises a serious concern regarding sensitive speech reconstruction, and speaker or gender identification by a malicious application having unrestricted access to motion sensor readings, without using the microphone. In this paper, we revisit this important line of research and closely inspect the effect of speech on smartphone motion sensors, in particular, gyroscope and accelerometer. First, we revisit the previously studied scenario (Michalevsky et al.; USENIX Security 2014), where the smartphone shares a common surface with a loudspeaker (with subwoofer) generating speech signals. We observe some effect on the motion sensor signals, which may indeed allow speaker and gender recognition to an extent. However, we also argue that the recorded effect on the sensor readings is possibly from conductive vibrations through the shared surface instead of direct acoustic vibrations due to speech as perceived in previous work. Second, we further extend the previous work by analyzing the effect of speech produced by (1) other less powerful speakers like the in-built laptop and smartphone speakers, and (2) live humans. Our experiments show that in-built laptop speakers were only able to affect the accelerometer when the laptop and the motion sensor shared a surface. Smartphone speakers were not found to be powerful enough to invoke a response in the motion sensors through aerial vibrations. We also report that in the presence of live human speech, we did not notice any effect on the motion sensor readings. Our results have two-fold implications. First, human-rendered speech seems potentially incapacitated to trigger smartphone motion sensors within the limited sampling rates imposed by the smartphone operating systems. Second, it seems that even machine-rendered speech may not be powerful enough to affect smartphone motion sensors through the aerial medium, although it may induce vibrations through a conductive surface that these sensors, especially accelerometer, could pick up if a relatively powerful speaker is used. Overall, our results suggest that smartphone motion sensors may pose a threat to speech privacy only in some limited scenarios.

Reducing the level of user effort involved in traditional two-factor authentication (TFA) constitutes an important research topic. A recent effort in this direction leverages ambient sounds to detect the proximity between the second factor device (phone) and the login terminal (browser), and eliminates the need for the user to transfer PIN codes. This approach is highly usable, but is completely vulnerable against far-near attackers, i.e., ones who are remotely located and can guess the victim's audio environment or make the phone create predictable sounds (e.g., ringers), and those who are in physical proximity of the user.

Establishing secure voice, video and text over Internet (VoIP) communications is a crucial task necessary to prevent eavesdropping and man-in-the-middle attacks. The traditional means of secure session establishment (e.g., those relying upon PKI or KDC) require a dedicated infrastructure and may impose unwanted trust onto third-parties. “Crypto Phones” (popular instances such as PGPfone and Zfone), in contrast, provide a purely peer-to-peer user-centric secure mechanism claiming to completely address the problem of wiretapping. The secure association mechanism in Crypto Phones is based on cryptographic protocols employing Short Authenticated Strings (SAS) validated over the voice medium. The security of Crypto Phones crucially relies on the assumption that the voice channel, over which SAS is validated, provides the properties of integrity and source authentication. In this paper, we challenge this assumption, and report on automated SAS voice imitation man-in-the-middle attacks that can compromise the security of Crypto Phones in both two-party and multi-party settings, even if users pay due diligence and even if an automated software ( voice biometrics systems) is used to detect voice manipulation. The first attack, called the short voice reordering attack, builds arbitrary SAS strings in a victim’s voice by reordering previously eavesdropped SAS strings spoken by the victim. The second attack, called the short voice morphing attack, builds arbitrary SAS strings in a victim’s voice from a few previously eavesdropped sentences (less than 3 minutes) spoken by the victim. We design and implement our attacks using off-the-shelf speech recognition/synthesis tools, and comprehensively evaluate them with respect to both manual detection (based on a user study with 30 participants) and automated detection via a speaker verification tool. The results demonstrate the effectiveness of our attacks against three prominent forms of SAS encodings: numbers, PGP word lists and Madlib sentences. These attacks can be used by a wiretapper to compromise the confidentiality and privacy of Crypto Phones voice, video and text communications (plus authenticity in case of text conversations).

Many widely used Internet messaging and calling apps, such as WhatsApp, Viber, Telegram, and Signal, have deployed an end-to-end encryption functionality. To defeat potential man-in-the-middle attackers against the key exchange protocol, the approach crucially relies upon users to perform a code verification task whereby each user must compare the code (a fingerprint of the cryptographic keys) computed by her app with the one computed by the other user's app and reject the session if the two codes do not match.

The goal of this work is to enable user authentication via finger inputs on ubiquitous surfaces leveraging low-cost physical vibration. We propose VibWrite that extends finger-input authentication beyond touch screens to any solid surface for smart access systems (e.g., access to apartments, vehicles or smart appliances). It integrates passcode, behavioral and physiological characteristics, and surface dependency together to provide a low-cost, tangible and enhanced security solution. VibWrite builds upon a touch sensing technique with vibration signals that can operate on surfaces constructed from a broad range of materials. It is significantly different from traditional password-based approaches, which only authenticate the password itself rather than the legitimate user, and the behavioral biometrics-based solutions, which usually involve specific or expensive hardware (e.g., touch screen or fingerprint reader), incurring privacy concerns and suffering from smudge attacks. VibWrite is based on new algorithms to discriminate fine-grained finger inputs and supports three independent passcode secrets including PIN number, lock pattern, and simple gestures by extracting unique features in the frequency domain to capture both behavioral and physiological characteristics such as contacting area, touching force, and etc. VibWrite is implemented using a single pair of low-cost vibration motor and receiver that can be easily attached to any surface (e.g., a door panel, a desk or an appliance). Our extensive experiments demonstrate that VibWrite can authenticate users with high accuracy (e.g., over 95% within two trials), low false positive rate (e.g., less 3%) and is robust to various types of attacks.

Crypto Phones aim to establish end-to-end secure voice (and text) communications based on human-centric (usually) short checksum validation. They require end users to perform: (1) checksum comparison to detect traditional data-based man-in-the-middle (data MITM) attacks, and, optionally, (2) speaker verification to detect sophisticated voice-based man-in-the-middle (voice MITM) attacks. However, research shows that both tasks are prone to human errors making Crypto Phones highly vulnerable to MITM attacks, especially to data MITM given the prominence of these attacks. Further, human errors under benign settings undermine usability since legitimate calls would often need to be rejected.

Password managers (aka stores or vaults) allow a user to store and retrieve (usually high-entropy) passwords for her multiple password-protected services by interacting with a "device" serving the role of the manager (e.g., a smartphone or an online third-party service) on the basis of a single memorable (low-entropy) master password. Existing password managers work well to defeat offline dictionary attacks upon web service compromise, assuming the use of high-entropy passwords is enforced. However, they are vulnerable to leakage of all passwords in the event the device is compromised, due to the need to store the passwords encrypted under the master password and/or the need to input the master password to the device (as in smartphone managers). Evidence exists that password managers can be attractive attack targets. In this paper, we introduce a novel approach to password management, called SPHINX, which remains secure even when the password manager itself has been compromised. In SPHINX, the information stored on the device is information theoretically independent of the user's master password - an attacker breaking into the device learns no information about the master password or the user's site-specific passwords. Moreover, an attacker with full control of the device, even at the time the user interacts with it, learns nothing about the master password - the password is not entered into the device in plaintext form or in any other way that may leak information on it. Unlike existing managers, SPHINX produces strictly high-entropy passwords and makes it compulsory for the users to register these randomized passwords with the web services, hence fully defeating offline dictionary attack upon service compromise. The design and security of SPHINX is based on the device-enhanced PAKE model of Jarecki et al. that provides the theoretical basis for this construction and is backed by rigorous cryptographic proofs of security. While SPHINX is suitable for different device and online platforms, in this paper, we report on its concrete instantiation on smartphones given their popularity and trustworthiness as password managers (or even two-factor authentication). We present the design, implementation and performance evaluation of SPHINX, offering prototype browser plugins, smartphone apps and transparent device-client communication. Based on our inspection analysis, the overall user experience of SPHINX improves upon current managers. We also report on a lab-based usability study of SPHINX, which indicates that users' perception of SPHINX security and usability is high and satisfactory when compared to regular password-based authentication. Finally, we discuss how SPHINX may be extended to an online service for the purpose of back-up or as an independent password manager.

An interesting approach to pairing devices involves the use of a vibrational channel, over which the keying material (e.g., a short PIN) is sent. This approach is efficient (only a unidirectional transfer of PIN is needed) and simple (the sending device requires a vibration motor and receiving device requires an accelerometer). However, it has been shown to be susceptible to acoustic emanations usually produced by the vibration motor. Recent research introduced a mechanism to defeat these attacks by attempting to mask the acoustic leakage with deliberate acoustic noises. In this paper, we pursue a systematic investigation of the security of such a "noisy vibrational pairing" mechanism in a strong yet realistic adversarial model where the eavesdropper is co-located with the victim device(s).

Deauthentication is an important component of any computing system that promises to offer legitimate access to restricted services residing on the system. As computing devices are ubiquitous, it has underscored the need to design zero-effort deauthentication systems from a usability perspective. While the design of such deauthentication systems is geared towards making them more usable, often the security implication of these deigns overlook the physical security of the system resulting in various side channel vulnerabilities in the system. This issue highlights the need to design a defense mechanism that is capable of minimizing the threat posed by such side channel attacks while having minimal impact on the design of the system.

In this paper, we study the neural underpinnings relevant to user-centered web security through the lens of functional near-infrared spectroscopy (fNIRS). Specifically, we design and conduct an fNIRS study to pursue a thorough investigation of users' processing of legitimate vs. illegitimate and familiar vs. unfamiliar websites. We pinpoint the neural activity in these tasks as well as the brain areas that control such activity. We show that, at the neurological level, users process the legitimate websites differently from the illegitimate websites when subject to phishing attacks. Similarly, we show that users exhibit marked differences in the way their brains process the previously familiar websites from unfamiliar websites. These findings have several defensive and offensive implications. In particular, we discuss how these differences may be used by the system designers in the future to differentiate between legitimate and illegitimate websites automatically based on neural signals. Similarly, we discuss the potential for future malicious attackers, with access to neural signals, in compromising the privacy of users by detecting whether a website is previously familiar or unfamiliar to the user.

Existing CAPTCHA solutions are a major source of user frustration on the Internet today, frequently forcing companies to lose customers and business. Game CAPTCHAs are a promising approach which may make CAPTCHA solving a fun activity for the user. One category of such CAPTCHAs – called Dynamic Cognitive Game (DCG) CAPTCHA – challenges the user to perform a game-like cognitive (or recognition) task interacting with a series of dynamic images. Specifically, it takes the form of many objects floating around within the images, and the user’s task is to match the objects corresponding to specific target(s), and drag/drop them to the target region(s). In this paper, we pursue a comprehensive analysis of DCG CAPTCHAs. We design and implement such CAPTCHAs, and dissect them across four broad but overlapping dimensions: (1) usability, (2) fully automated attacks, (3) human-solving relay attacks, and (4) hybrid attacks that combine the strengths of automated and relay attacks. Our study shows that DCG CAPTCHAs are highly usable, even on mobile devices and offer some resilience to relay attacks, but they are vulnerable to our proposed automated and hybrid attacks.

The current Android sensor security model either allows only restrictive read access to sensitive sensors (e.g., an app can only read its own touch data) or requires special install-time permissions (e.g., to read microphone, camera, or GPS). Moreover, Android does not allow write access to any of the sensors. Sensing-based security and non-security applications, therefore, crucially rely upon the sanity of the Android sensor security model. In this paper, we show that such a model can be effectively circumvented. Specifically, we build SMASheD, a legitimate framework under the current Android ecosystem that can be used to stealthily sniff as well as manipulate many of the Android's restricted sensors (even touch input). SMASheD exploits the Android debug bridge functionality and enables a malicious app with only the INTERNET permission to read, and write to, multiple different sensor data files at will. SMASheD is the first framework, to the best of our knowledge, that can sniff and manipulate protected sensors on unrooted Android devices, without user awareness, without constant device-PC connection and without the need to infect the PC. The primary contributions of this paper are twofold. First, we design and develop the SMASheD framework, and evaluate its effectiveness on multiple Android devices, including phones, watches, and glasses. Second, as an offensive implication of the SMASheD framework, we introduce a wide array of potentially devastating attacks. Our attacks against the touch sensor range from accurately logging the touchscreen input (TouchLogger) to injecting touch events for accessing restricted sensors and resources, installing and granting special permissions to other malicious apps, accessing user accounts, and authenticating on behalf of the user-essentially almost doing whatever the device user can do (secretively). Our attacks against various physical sensors (motion, position, and environmental) can subvert the functionality provided by numerous existing sensing-based security and non-security applications, including those used for (continuous) authentication, authorization, safety, and elderly care.

We introduce a setting that we call Device-Enhanced PAKE (DE-PAKE), where PAKE (password-authenticated key exchange) protocols are strengthened against online and offline attacks through the use of an auxiliary device that aids the user in the authentication process. We build such schemes and show that their security, properly formalized, achieves maximal-attainable resistance to online and offline attacks in both PKI and PKI-free settings. In particular, an online attacker must guess the user's password and also corrupt the user's auxiliary device to authenticate, while an attacker who corrupts the server cannot learn the users' passwords via an offline dictionary attack. Notably, our solutions do not require secure channels, and nothing (in an information-theoretic sense) is learned about the password by the device (or a malicious software running on the device) or over the device-client channel, even without any external protection of this channel. An attacker taking over the device still requires a full online attack to impersonate the user. Importantly, our DE-PAKE scheme can be deployed at the user end without the need to modify the server and without the server having to be aware that the user is using a DE-PAKE scheme. In particular, the schemes can work with standard servers running the usual password-over-TLS authentication. We use these protocols to implement a practical DE-PAKE system and we evaluate its performance. To improve usability the implemented system utilizes automated and user-transparent data channel between the mobile device and the client, falling back to localized communication if the device looses primary connectivity.

Reducing user burden underlying traditional two-factor authentication constitutes an important research effort. An interesting representative approach, Sound-Proof, leverages ambient sounds to detect the proximity between the second factor device (phone) and the login terminal (browser). Sound-Proof was shown to be secure against remote attackers and highly usable, and is now under early deployment phases. In this paper, we identify a weakness of the Sound-Proof system, namely, the remote attacker does not have to predict the ambient sounds near the phone as assumed in the Sound-Proof paper, but rather can deliberately make-or wait for-the phone to produce predictable or previously known sounds (e.g., ringer, notification or alarm sounds). Exploiting this weakness, we build Sound-Danger, a full attack system that can successfully compromise the security of Sound-Proof. The attack involves buzzing the victim user's phone, or waiting for the phone to buzz, and feeding the corresponding sounds at the browser to login on behalf of the user. The attack works precisely under Sound-Proof's threat model.

Pairing between wireless devices may be secured by the use of an auxiliary channel such as audio, visuals or vibrations. A simple approach to pairing involves one of the devices initiating the transmission of a key, or keying material like a short password, over the auxiliary channel to the other device. A successful pairing is achieved when the receiving device is able to decode the key without any errors while the attacker is unable to eavesdrop the key.

Recent research shows that it is possible to infer a user's touchscreen inputs (e.g., passwords) on Android devices based on inertial (motion/position) sensors, currently freely-accessible by any Android app. Given the high accuracies of such touchstroke logging attacks, they are now considered a significant threat to user privacy. Consequently, the security community has started exploring defenses to such side channel attacks, but the suggested solutions are either not effective (e.g., those based on vibrational noise) and/or may significantly undermine system usability (e.g., those based on keyboard layout randomization).

The security of computer systems often relies upon decisions and actions of end users. In this paper, we set out to investigate users' susceptibility to cybercriminal attacks by concentrating at the most fundamental component governing user behavior-the human brain. We introduce a novel neuroscience-based study methodology to inform the design of user-centered security systems as it relates to cybercrime. In particular, we report on an functional magnetic resonance imaging study measuring users' security performance and underlying neural activity with respect to two critical security tasks: (1) distinguishing between a legitimate and a phishing website and (2) heeding security (malware) warnings. We identify the neural markers that might be controlling users' performance in these tasks, and establish relationships between brain activity and behavioral performance as well as between users' personality traits and security behavior. Our results provide a largely positive perspective on users' capability and performance vis-à-vis these crucial security tasks. First, we show that users exhibit significant brain activity in key regions associated with decision-making, attention, and problem-solving (phishing and malware warnings) as well as language comprehension and reading (malware warnings), which means that users are actively engaged in these security tasks. Second, we demonstrate that certain individual traits, such as impulsivity measured via an established questionnaire, are associated with a significant negative effect on brain activation in these tasks. Third, we discover a high degree of correlation in brain activity (in decision-making regions) across phishing detection and malware warnings tasks, which implies that users' behavior in one task may potentially be predicted by their behavior in the other. Fourth, we discover high functional connectivity among the core regions of the brain, while users performed the phishing detection task. Finally, we discuss the broader impacts and implications of our work on the field of user-centered security, including the domain of security education, targeted security training, and security screening.

CAPTCHAs represent an important pillar in the web security domain. Yet, current CAPTCHAs do not fully meet the web security requirements. Many existing CAPTCHAs can be broken using automated attacks based on image processing and machine learning techniques. Moreover, most existing CAPTCHAs are completely vulnerable to human-solver relay attacks, whereby CAPTCHA challenges are simply outsourced to a remote human solver.

Crypto Phones represent an important approach for end-to-end VoIP security, claiming to prevent "wiretapping" and session hijacking attacks without relying upon third parties. In order to establish a secure session, Crypto Phones rely upon end users to perform two tasks: (1) checksum comparison: verbally communicating and matching short checksums displayed on users' devices, and (2) speaker verification: ascertaining that the voice announcing the checksum is the voice of the legitimate user at the other end. However, the human errors in executing these tasks may adversely affect the security and usability of Crypto Phones. Particularly, failure to detect mismatching checksums or imitated voices would result in a compromise of Crypto Phones session communications.

Detecting phishing attacks (identifying fake vs. real websites) and heeding security warnings represent classical user-centered security tasks subjected to a series of prior investigations. However, our understanding of user behavior underlying these tasks is still not fully mature, motivating further work concentrating at the neuro-physiological level governing the human processing of such tasks.

In this paper, we present a run-time defense to the malware that inspects the presence/absence of certain transparent human gestures exhibited naturally by users prior to accessing a desired resource. Specifically, we focus on the use of transparent gestures to prevent the misuse of three critical smartphone capabilities - the phone calling service, the camera resource and the NFC reading functionality. We show how the underlying natural hand movement gestures associated with the three services, calling, snapping and tapping, can be detected in a robust manner using multiple - motion, position and ambient - sensors and machine learning classifiers. To demonstrate the effectiveness of our approach, we collect data from multiple phone models and multiple users in real-life or near real-life scenarios emulating both benign settings as well as adversarial scenarios. Our results show that the three gestures can be detected with a high overall accuracy, and can be distinguished from one another and from other activities (benign or malicious), serving as a viable malware defense. In the future, we believe that transparent gestures associated with other smartphone services, such as sending SMS or email, can also be integrated with our system.

Malware is a burgeoning threat for smartphones and continuing advancing. Traditional defenses to malware, however, are not suitable for smartphones due to their resource intensive nature. This necessitates the design of novel mechanisms that can consider the specifics of the smartphone malware and smartphones themselves. In this paper, we introduce a lightweight permission enforcement approach-Tap-Wave-Rub (TWR)-for smartphone malware prevention. TWR is based on simple cyber-physical human interactions, i.e., human gestures, that are very quick and intuitive but less likely to be exhibited in users' daily activities. Presence or absence of such gestures, prior to accessing an application, can effectively inform the OS whether the access request is benign or malicious. In particular, we present the design of two mechanisms: 1) acceleration-based phone tapping detection and 2) proximity-based finger tapping, rubbing, or hand waving detection. The first mechanism is geared for near field communication applications, which usually require the user to tap her phone with another device. The second mechanism involves very simple gestures, i.e., tapping or rubbing a finger near the top of phone's screen or waving a hand close to the phone, and broadly appeals to many applications (e.g., SMS). In addition, we present the TWR-enhanced Android permission model, the prototypes implementing the underlying gesture recognition mechanisms, and a variety of novel experiments to evaluate these mechanisms. Our results suggest the proposed approach could be very effective for malware detection/prevention, with quite low false positives and false negatives, while imposing little to no additional burden on the users.

Existing captcha solutions on the Internet are a major source of user frustration. Game captchas are an interesting and, to date, little-studied approach claiming to make captcha solving a fun activity for the users. One broad form of such captchas -- called Dynamic Cognitive Game (DCG) captchas -- challenge the user to perform a game-like cognitive task interacting with a series of dynamic images. We pursue a comprehensive analysis of a representative category of DCG captchas. We formalize, design and implement such captchas, and dissect them across: (1) fully automated attacks, (2) human-solver relay attacks, and (3) usability. Our results suggest that the studied DCG captchas exhibit high usability and, unlike other known captchas, offer some resistance to relay attacks, but they are also vulnerable to our novel dictionary-based automated attack.

Establishing secure voice, video and text over Internet (VoIP) communications is a crucial task necessary to prevent eavesdropping and man-in-the-middle attacks. The traditional means of secure session establishment (e.g., those relying upon PKI or KDC) require a dedicated infrastructure and may impose unwanted trust onto third-parties. "Crypto Phones" (popular instances such as PGPfone and Zfone), in contrast, provide a purely peer-to-peer user-centric secure mechanism claiming to completely address the problem of wiretapping. The secure association mechanism in Crypto Phones is based on cryptographic protocols employing Short Authenticated Strings (SAS) validated by end users over the voice medium. The security of Crypto Phones crucially relies on the assumption that the voice channel, over which SAS is validated by the users, provides the properties of integrity and source authentication. In this paper, we challenge this assumption, and report on automated SAS voice imitation man-in-the-middle attacks} that can compromise the security of Crypto Phones in both two-party and multi-party settings, even if users pay due diligence. The first attack, called the short voice reordering attack, builds arbitrary SAS strings in a victim's voice by reordering previously eavesdropped SAS strings spoken by the victim. The second attack, called the short voice morphing attack, builds arbitrary SAS strings in a victim's voice from a few previously eavesdropped sentences (less than 3 minutes) spoken by the victim. We design and implement our attacks using off-the-shelf speech recognition/synthesis tools, and comprehensively evaluate them with respect to both manual detection (via a user study with 30 participants) and automated detection. The results demonstrate the effectiveness of our attacks against three prominent forms of SAS encodings: numbers, PGP word Lists and Madlib sentences. These attacks can be used by a wiretapper to compromise the confidentiality and privacy of Crypto Phones voice, video and text communications (plus authenticity in case of text conversations).

Dynamic Cognitive Game (DCG) CAPTCHAs are a promising new generation of interactive CAPTCHAs aiming to provide improved security against automated and human-solver relay attacks. Unlike existing CAPTCHAs, defeating DCG CAPTCHAs using pure automated attacks or pure relay attacks may be challenging in practice due to the fundamental limitations of computer algorithms (semantic gap) and synchronization issues with solvers. To overcome this barrier, we propose two hybrid attack frameworks. which carefully combine the strengths of an automated program and offline/online human intelligence. These hybrid attacks require maintaining the synchronization only between the game and the bot similar to a pure automated attack, while solving the static AI problem (i.e., bridging the semantic gap) behind the game challenge similar to a pure relay attack. As a crucial component of our framework, we design a new DCG object tracking algorithm, based on color code histogram, and show that it is simpler, more efficient and more robust compared to several known tracking approaches. We demonstrate that both frameworks can effectively defeat a wide range of DCG CAPTCHAs.

Zero-Interaction Authentication (ZIA) refers to approaches that authenticate a user to a verifier (terminal) without any user interaction. Currently deployed ZIA solutions are predominantly based on the terminal detecting the proximity of the user's personal device, or a security token, by running an authentication protocol over a short-range wireless communication channel. Unfortunately, this simple approach is highly vulnerable to low-cost and practical relay attacks which completely offset the usability benefits of ZIA. The use of contextual information, gathered via on-board sensors, to detect the co-presence of the user and the verifier is a recently proposed mechanism to resist relay attacks. In this paper, we systematically investigate the performance of different sensor modalities for co-presence detection with respect to a standard Dolev-Yao adversary. First, using a common data collection framework run in realistic everyday settings, we compare the performance of four commonly available sensor modalities (WiFi, Bluetooth, GPS, and Audio) in resisting ZIA relay attacks, and find that WiFi is better than the rest. Second, we show that, compared to any single modality, fusing multiple modalities improves resilience against ZIA relay attacks while retaining a high level of usability. Third, we motivate the need for a stronger adversarial model to characterize an attacker who can compromise the integrity of context sensing itself. We show that in the presence of such a powerful attacker, each individual sensor modality offers very low security. Positively, the use of multiple sensor modalities improves security against such an attacker if the attacker cannot compromise multiple modalities simultaneously.

Web Search is one of the most rapidly growing applications on the internet today. However, the current practice followed by most search engines – of logging and analyzing users' queries – raises serious privacy concerns. In this paper, we concentrate on two existing solutions which are relatively easy to deploy – namely Query Obfuscation and Anonymizing Networks. In query obfuscation, a client-side software attempts to mask real user queries via injection of certain noisy queries. Anonymizing networks route the user queries through a series of relay servers, hiding the actual query source from the search engine. A fundamental problem with these solutions, however, is that user queries are still obviously revealed to the search engine, although they are “mixed” among queries generated either by a machine or by other users. We focus on TrackMeNot (TMN), a popular query obfuscation tool, and the Tor anonymizing network, and try to analyse whether these solutions can actually preserve users' privacy in practice against an adversarial search engine. We demonstrate that a search engine, equipped with only a short-term history of a user's search queries, can break the privacy guarantees of TMN and Tor by only utilizing off-the-shelf machine learning techniques.

ABSTRACT Radio frequency identification (RFID) systems are becoming increasingly ubiquitous in both public and private domains. However, because of the inherent weaknesses of underlying wireless radio communications, RFID systems are plagued with a wide variety of security and privacy threats. A large number of these threats arise because of the tag's promiscuous response to any reader requests. This renders sensitive tag information easily subject to unauthorized reading. Promiscuous tag response also incites different forms of relay attacks whereby a malicious colluding pair, relaying messages between a tag and a reader, can successfully impersonate the tag without actually possessing it. Because of the increasing ubiquity of RFID devices, there is a pressing need for the development of security primitives and protocols to defeat unauthorized reading and relay attacks. However, currently deployed or proposed solutions often fail to satisfy the constraints and requirements of the underlying RFID applications in terms of (one or more of) efficiency, security, and usability. This paper proposes a novel research direction, one that utilizes sensing technologies, to tackle the problems of unauthorized reading and relay attacks with a goal of reconciling the requirements of efficiency, security, and usability. The premise of the proposed work is based on a current technological advancement that enables many RFID tags with low‐cost sensing capabilities. The on‐board tag sensors will be used to acquire useful contextual information about the tag's environment (or its owner, or the tag itself). For defense against unauthorized reading and relay attacks, such context information can be leveraged in two ways. First, contextual information can be used to design context‐aware selective unlocking mechanisms so that tags can selectively respond to reader interrogations and thus minimize the likelihood of unauthorized reading and “ghost‐and‐leech” relay attacks. Second, contextual information can be used as a basis for context‐aware secure transaction verification to defend against special types of relay attacks involving malicious readers. Copyright © 2011 John Wiley & Sons, Ltd.

The proliferation of mobile computing devices has enabled immense opportunities for everyday users. At the same time, however, this has opened up new, and perhaps more severe, possibilities for attacks. In this paper, we explore a novel generation of mobile malware that exploits the rich variety of sensors available on current mobile devices.

We introduce a lightweight permission enforcement approach - Tap-Wave-Rub (TWR) - for smartphone malware prevention. TWR is based on simple human gestures (implicit or explicit) that are very quick and intuitive but less likely to be exhibited in users' daily activities. Presence or absence of such gestures, prior to accessing an application, can effectively inform the OS whether the access request is benign or malicious. In this paper, we focus on the design of an accelerometer-based phone tapping detection mechanism. This implicit tapping detection mechanism is geared to prevent malicious access to NFC services, where a user is usually required to tap her phone with another device. We present a variety of novel experiments to evaluate the proposed mechanism. Our results suggest that our approach could be very effective for malware prevention, with quite low false positives and false negatives, while imposing no additional burden on the users. As part of the TWR framework, we also briefly explore explicit gestures (finger tapping, rubbing or hand waving based on proximity sensor), which could be used to protect services which do not have a unique implicit gesture associated with them.

In this paper, we report on a new approach for enhancing security and privacy in certain RFID applications whereby location or location-related information (such as speed) can serve as a legitimate access context. Examples of these applications include access cards, toll cards, credit cards, and other payment tokens. We show that location awareness can be used by both tags and back-end servers for defending against unauthorized reading and relay attacks on RFID systems. On the tag side, we design a location-aware selective unlocking mechanism using which tags can selectively respond to reader interrogations rather than doing so promiscuously. On the server side, we design a location-aware secure transaction verification scheme that allows a bank server to decide whether to approve or deny a payment transaction and detect a specific type of relay attack involving malicious readers. The premise of our work is a current technological advancement that can enable RFID tags with low-cost location (GPS) sensing capabilities. Unlike prior research on this subject, our defenses do not rely on auxiliary devices or require any explicit user involvement.

Secure “pairing” of wireless devices based on auxiliary or out-of-band (OOB)-audio, visual, or tactile-communication is a well-established research direction. Specifically, authenticated as well as secret OOB (AS-OOB) channels have been shown to be quite useful for this purpose. Pairing can be achieved by simply transmitting the key or short password over the AS-OOB channel, avoiding potential serious human errors. This paper analyzes the security of AS-OOB pairing. Specifically, we take a closer look at three notable prior AS-OOB pairing proposals and challenge the assumptions upon which the security of these proposals relies, i.e., the secrecy of underlying audio channels. The first proposal (IMD Pairing) uses a low frequency audio channel to pair an implanted RFID tag with an external reader. The second proposal (PIN-Vibra) uses an automated vibrational channel to pair a mobile phone with a personal RFID tag. The third proposal (BEDA) uses vibration (or blinking) on one device and manually synchronized button pressing on another device or simultaneous button pressing on two devices. We demonstrate the feasibility of eavesdropping over acoustic emanations associated with these methods and conclude that they provide a weaker level of security than was originally assumed or desired for the pairing operation.

We take a closer look at keyboard acoustic emanations specifically for the purpose of eavesdropping over random passwords. In this scenario, dictionary and HMM language models are not applicable; the attacker can only utilize the raw acoustic information which has been recorded. We investigate several existing signal processing techniques for our purpose, and introduce a novel technique -- time-frequency decoding -- that improves the detection accuracy compared to previous techniques. We also carefully examine the effect of typing style -- a crucial variable largely ignored by prior research -- on the detection accuracy. Our results show that using the same typing style (hunt and peck) for both training and decoding the data, the best case success rate for detecting correctly the typed key is 64% per character. The results also show that changing the typing style, to touch typing, during the decoding stage reduces the success rate, but using the time-frequency technique, we can still achieve a success rate of around 40% per character.

We perform a large-scale study to quantify just how severe the privacy leakage problem is in Facebook. As a case study, we focus on estimating birth year, which is a fundamental human attribute and, for many people, a private one. Specifically, we attempt to estimate the birth year of over 1 million Facebook users in New York City. We examine the accuracy of estimation procedures for several classes of users: (i) highly private users, who do not make their friend lists public; (ii) users who hide their birth years but make their friend lists public. To estimate Facebook users' ages, we exploit the underlying social network structure to design an iterative algorithm, which derives age estimates based on friends' ages, friends of friends' ages, and so on. We find that for most users, including highly private users who hide their friend lists, it is possible to estimate ages with an error of only a few years. We also make a specific suggestion to Facebook which, if implemented, would greatly reduce privacy leakages in its service.

Many RFID tags store valuable information privy to their users that can easily be subject to unauthorized reading, leading to owner tracking or impersonation. RFID tags are also susceptible to different forms of relay attacks. This paper presents novel sensing-enabled defenses to unauthorized reading and relay attacks against RFID systems without necessitating any changes to the traditional RFID usage model. More specifically, the paper proposes the use of on-board tag sensors to (automatically) acquire useful contextual information about the tag's environment (or its owner, or the tag itself). It suggests how this information can be used to achieve two security functionalities. First, such context recognition can be leveraged for the purpose of selective tag unlocking - the tag will respond selectively to reader interrogations, i.e., only when it is deemed safe to do so. Second, context recognition can be used as a basis for transaction verification in order to provide protection against a severe form of relay attacks involving malicious RFID readers. To demonstrate the feasibility of the overall idea, a novel selective unlocking mechanism based on owner's posture recognition is presented. The evaluation of the proposed mechanism shows its effectiveness in significantly raising the bar against many different RFID attacks.

In this paper, we report on a new approach for enhancing security and privacy in certain RFID applications whereby location or location-related information (such as speed) can serve as a legitimate access context. Examples of these applications include access cards, toll cards, credit cards and other payment tokens. We show that location awareness can be used by both tags and back-end servers for defending against unauthorized reading and relay attacks on RFID systems. On the tag side, we design a location-aware selective unlocking mechanism using which tags can selectively respond to reader interrogations rather than doing so promiscuously. On the server side, we design a location-aware secure transaction verification scheme that allows a bank server to decide whether to approve or deny a payment transaction and detect a specific type of relay attack involving malicious readers. The premise of our work is a current technological advancement that can enable RFID tags with low-cost location (GPS) sensing capabilities. Unlike prior research on this subject, our defenses do not rely on auxiliary devices or require any explicit user involvement.

HASH(0x3b6160c)

We revisit the problem of protecting user's private data against adversarial compromise of user's device(s) which store this data. We formalize the solution we propose as Password-Protected Secret-Sharing (PPSS), which allows a user to secret-share her data among n trustees in such a way that (1) the user can retrieve the shared secret upon entering a correct password into a reconstruction protocol, which succeeds as long as at least t+1 uncorrupted trustees are accessible, and (2) the shared data remains secret even if the adversary which corrupts t trustees, with the level of protection expected of password-authentication, i.e. the probability that the adversary learns anything useful about the secret is at most q/|D| where q is the number of reconstruction protocol the adversary manages to trigger and |D| is the size of the password dictionary. We propose an efficient PPSS protocol in the PKI model, secure under the DDH assumption, using non-interactive zero-knowledge proofs with efficient instantiations in the Random Oracle Model. Our protocol is practical, with fewer than 16 exponentiations per trustee and 8t+17 exponentiations per user, with O(1) bandwidth between the user and each trustee, and only three message flows, implying a single round of interaction in the on-line phase. As a side benefit our PPSS protocol yields a new Threshold Password Authenticated Key Exchange (T-PAKE) protocol in the PKI model with significantly lower message, communication, and server computation complexities then existing T-PAKE's.

Web search has emerged as one of the most important applications on the internet, with several search engines available to the users. There is a common practice among these search engines to log and analyse the user queries, which leads to serious privacy implications. One well known solution to search privacy involves issuing the queries via an anonymizing network, such as Tor, thereby hiding one's identity from the search engine. A fundamental problem with this solution, however, is that user queries are still obviously revealed to the search engine, although they are "mixed" among the queries issued by other users of the same anonymization service.

When users wish to establish wireless radio communication between/among their devices, the channel has to be bootstrapped first. The process of setting up a secure communication channel between two previously unassociated devices is referred to as "Secure Device Pairing". The focus of prior research on this topic has mostly been limited to "personal pairing" scenarios, whereby a single user controls both the devices. In this paper, we instead consider "social pairing" scenarios, whereby two different users establish pairing between their respective devices. We present a comprehensive study to identify methods suitable for social pairing, and comparatively evaluate the usability and security of these methods. Our results identify methods best-suited for users, in terms of efficiency, error-tolerance and of course, usability. Our work provides insights on the applicability and usability of methods for emerging social pairing scenarios, a topic largely ignored so far.

A promising approach to location privacy is query obfuscation, which involves reporting k -- 1 false locations along with the real location. In this paper, we examine the level of privacy protection provided by the current query obfuscation techniques against adversarial location service providers. As a representative and realistic implementation of query obfuscation, we focus on SybilQuery. We present two types of attacks depending upon whether or not a short-term query history is available. When history is available, using machine learning, we were able to identify 93.67% of user trips, with only 2.02% of fake trips misclassified, for the security parameter k = 5. In the absence of history, we used trip correlations to form a smaller set of trips effectively increasing the user query identification probability from 20% to about 40%. Our work demonstrates that the use of aggregate statistical information alone is not sufficient to generate simulated trips. We identify areas for improvement in the existing query obfuscation techniques.

Personal RFID tags store valuable information private to their users that can easily be subject to eavesdropping, unauthorized reading, owner tracking, and cloning. RFID tags are also susceptible to relay attacks and likely to get lost and stolen. In this paper, we introduce the problem of user authentication to RFID tags. This allows users to control when and where their RFID tags can be accessed. We present a novel approach for user authentication to multiple RFID tags called “Vibrate-to-Unlock” (VtU). This technique uses a mobile phone as an authentication token, forming an unidirectional tactile communication channel between users and their RFID tags. Authenticating to an RFID tag involves touching a vibrating phone to the tag or an object carrying the tag, such as a wallet. We discuss the design and implementation of this new method on Intel's WISP tags. We also report on a preliminary usability evaluation of our VtU prototype.

Accelerometers are versatile sensors that are nearly ubiquitous. They are available on a wide variety of devices and are particularly common on those that are mobile or have wireless capabilities. Accelerometers are applicable in a number of settings and circumstances, including important security and privacy domains. In this paper, we investigate the use of accelerometers for the purpose of true random number generation. As our first contribution, we discover that an accelerometer possesses two unique and appealing properties when used as an entropy source. First, contrary to intuition, an accelerometer can derive sufficient entropy even when it is stationary (i.e., not subject to perceivable acceleration). Next, and more importantly, the entropy of a stationary accelerometer can not be reduced in the presence of a variety of environmental variations or even under adversarial manipulations. This means that, unlike other sensors, accelerometers are resistant to changing environments, benign or otherwise. To support this claim, we develop a thorough experimental adversarial model for accelerometers that supply a system with entropy. To the authors' knowledge, this is the first real world model in the context of entropy collection. As our second contribution, we demonstrate the validity of accelerometer based random number generation on an RFID tag, which is a highly resource constrained device. We present the design and implementation of our method on an Intel WISP tag and conduct several novel experiments to evaluate its feasibility. Our results indicate that a high quality 128-bit random number can be extracted using an accelerometer in about 1.5 seconds even when the sensor is in a stationary state. To our knowledge, this is the first random number generation technique that is known to be viable for RFID devices based on general-purpose hardware.

An RFID reader must authenticate its designated tags in order to prevent tag forgery and counterfeiting. At the same time, due to privacy requirements of many applications, a tag should remain anonymous and untraceable to an adversary during the authentication process. In this paper, we propose an “HB-like” protocol for privacy-preserving authentication of RFID tags. Previous protocols for privacy-preserving authentication were based on PRF computations. Our protocol can instead be used on low-cost tags that may be incapable of computing traditional PRFs. Moreover, since the underlying computations in HB protocols are very efficient, our protocol also reduces reader-side load compared to PRF-based protocols. We suggest a tree-based approach that replaces the PRF-based authentication from prior work with a procedure such as HB+ or HB#. We optimize the tree-traversal stage through usage of a “light version” of the underlying protocol and shared random challenges across all levels of the tree. This provides significant reduction of the communication resources, resulting in a privacy-preserving protocol almost as efficient as the underlying HB+ or HB#. We also present analytical and simulation results comparing our method with prior proposals in terms of computation, communication and memory overheads.

“Pairing” is the establishment of authenticated key agreement between two devices over a wireless channel. Such devices are ad hoc in nature as they lack any common preshared secrets or trusted authority. Fortunately, these devices can be connected via auxiliary physical (audio, visual, tactile) channels which can be authenticated by human users. They can, therefore, be used to form the basis of a pairing operation. Recently proposed pairing protocols and methods are based upon bidirectional physical channels. However, various pairing scenarios are asymmetric in nature, i.e., only a unidirectional physical channel exists between two devices (such as between a cell phone and an access point). In this paper, we show how strong mutual authentication can be achieved even with a unidirectional visual channel, where prior methods could provide only a weaker property termed as presence. This could help reduce the execution time and improve usability of prior pairing methods. In addition, by adopting recently proposed improved pairing protocols, we propose how visual channel authentication can be used even on devices that have very limited displaying capabilities, all the way down to a device whose display consists of a cheap single light-source, such as a light-emitting diode. We present the results of a preliminary usability study evaluating our proposed method.

Secure "pairing" of wireless devices based on auxiliary or out-of-band (OOB) - audio, visual or tactile - communication is a well-established research direction. Lack of good quality interfaces on or physical access to certain constrained devices (e.g., headsets, access points, medical implants) makes pairing a challenging problem in practice. Prior work shows that pairing of constrained devices based on authenticated OOB (A-OOB) channels can be prone to human errors that eventually translate into man-in-the-middle attacks. An alternative and more usable solution is to use OOB channel(s) that are authenticated as well as secret (AS-OOB). AS-OOB pairing can be achieved by simply transmitting the key or a short password over the AS-OOB channel, avoiding potential serious human errors.

A fairly common modern setting entails users, each in possession of a personal wireless device, wanting to communicate securely, via their devices. If these users (and their devices) have no prior association, a new security context must be established. In order to prevent potential attacks, the initial context (association) establishment process must involve only the intended devices and their users.

When a host discovers that it has a software vulnerability that is susceptible to an attack, the host needs to obtain and install a patch. Because centralized distribution of patches may not scale well, peer-to-peer (P2P) approaches have recently been suggested. There is, however, a serious privacy problem with peer-assisted patch distribution: when a peer A requests a patch from another peer B, it announces to B its vulnerability, which B can exploit instead of providing the patch. Through analytical modeling and simulation, we show that a large majority of vulnerable hosts will typically become compromised with a basic design for peer- assisted patch distribution. We then study the effectiveness of two different approaches in countering this privacy problem. The first approach utilizes special-purpose peer nodes, referred to as honeypots, that discover and blacklist malicious peers listening for patch requests from susceptible hosts. In the second approach, the patches are requested through an anonymizing network, hiding the identities of susceptible hosts from malicious peers. Using analytical models and simulation, we show that, honeypots do not completely solve the privacy problem; in contrast, an anonymizing network turns out to be more suitable for security patch distribution.

Access control is the fundamental security service in ad hoc groups. It is needed not only to prevent unauthorized entities from joining the group, but also to bootstrap other security services. Luo,proposed a set of protocols for providing ubiquitous and robust access control (called URSA) in mobile ad hoc networks without relying on a centralized authority. The URSA protocol relies on the new proactive RSA signature scheme, which allows members in an ad hoc group to make access control decisions in a distributed manner. The proposed proactive RSA signature scheme is assumed secure as long as no more than an allowed threshold of participating members is simultaneously corrupted at any point in the lifetime of the scheme. In this paper, we show an attack on this proposed proactive RSA scheme, in which an admissible threshold of malicious group members can completely recover the group RSA secret key in the course of the lifetime of this scheme. Our attack stems from the fact that the threshold signature protocol which is a part of this proactive RSA scheme leaks some seemingly innocuous information about the secret signature key. We show how the corrupted members can influence the execution of the scheme in such a way so that the slowly leaked information is used to reconstruct the entire shared secret.

ldquoSecure Device Pairingrdquo is the process of bootstrapping a secure channel between two previously unassociated devices over a (usually wireless) human-imperceptible communication channel. Lack of prior security context and common trust infrastructure open the door for Man-in-the-Middle (also known as Evil Twin) attacks. Mitigation of these attacks requires user involvement in the device pairing process. Prior research yielded a number of interesting methods utilizing various auxiliary human-perceptible channels, e.g., visual, acoustic or tactile. These methods engage the user in authenticating information exchanged over human-imperceptible channels, thus mitigating MiTM attacks and forming the basis for secure pairing. We present the first comprehensive comparative evaluation of notable secure device pairing methods. Our results identify methods best-suited for a given combination of devices and human abilities. This work is both important and timely, since it sheds light on usability in one of the very few settings where a wide range of users (not just specialists) are confronted with security techniques.

No abstract available.

Mobile ad hoc networks (MANETs) have many well-known applications in military settings as well as in emergency and rescue operations. However, a lack of infrastructure and centralized control make MANETs inherently insecure, and therefore specialized security services are needed for their deployment.Self-certificationis an essential and fundamental security service in MANETs. It is needed to securely cope with dynamic membership and topology, and to bootstrap other important security primitives and services (such as secure routing and group key management) without the assistance of any centralized trusted authority. An ideal protocol must involve minimal interaction among the MANET nodes, since connectivity can be unstable. Also, since MANETs are often composed of weak or resource-limited devices, a self-certification protocol must be efficient in terms of computation and communication. In this paper, we propose a power-aware and fully noninteractive self-certification protocol based on bivariate polynomial secret sharing and a noninteractive threshold signature scheme. In contrast with prior work, our techniques do not require any interaction and do not involve any costly reliable broadcast communication among MANET nodes. We thoroughly analyze our proposal and show that it compares favorably to previous mechanisms.

Decentralized node admission is an essential and fundamental security service in mobile ad hoc networks (MANETs). It is needed to securely cope with dynamic membership and topology as well as to bootstrap other important security primitives (such as key management) and services (such as secure routing) without the assistance of any centralized trusted authority. An ideal admission technique must involve minimal interaction among MANET nodes, since connectivity can be unstable. Also, since MANETs are often composed of weak or resource-limited devices, admission must be efficient in terms of computation and communication. Most previously proposed admission protocols are prohibitively expensive and require heavy interaction among MANET nodes. In this paper, we focus on a common type of MANET that is formed on a temporary basis, and present a secure, efficient, and a fully noninteractive admission technique geared for this type of a network. Our admission protocol is based on secret sharing techniques using bivariate polynomials. We also present a new scheme that allows any pair of MANET nodes to efficiently establish an on-the-fly secure communication channel.

The operation of achieving authenticated key agreement between two human-operated devices over a short-range wireless communication channel (such as Bluetooth or WiFi) is referred to as "Pairing". The devices in such a scenario are ad hoc in nature, i.e., they can neither be assumed to have a prior context (such as pre-shared secrets) with each other nor do they share a common trusted on- or off-line authority. However, the devices can generally be connected using auxiliary physical channel(s) (such as audio, visual, etc.) that can be authenticated by the device user(s) and thus form a basis for pairing.

Recently several researchers and practitioners have begun to address the problem of how to set up secure communication between two devices without the assistance of a trusted third party. McCune et al., (2005) proposed that one device displays the hash of its public key in the form of a barcode, and the other device reads it using a camera. Mutual authentication requires switching the roles of the devices and repeating the above process in the reverse direction. In this paper, we show how strong mutual authentication can be achieved even with a unidirectional visual channel, without having to switch device roles. By adopting recently proposed improved pairing protocols, we propose how visual channel authentication can be used even on devices that have very limited displaying capabilities

Admission control is an essential and fundamental security service in mobile ad hoc networks (MANETs). It is needed to securely cope with dynamic membership and topology and to bootstrap other important security primitives (such as key management) and services (such as secure routing) without the assistance of any centralized trusted authority. An ideal admission protocol must involve minimal interaction among the MANET nodes, since connectivity can be unstable. Also, since MANETs are often composed of weak or resource-limited devices, admission control must be efficient in terms of computation and communication. Most previously proposed admission control protocols are prohibitively expensive and require a lot of interaction among MANET nodes in order to securely reach limited consensus regarding admission and cope with potentially powerful adversaries. While the expense may be justified for long-lived group settings, short-lived MANETs can benefit from much less expensive techniques without sacrificing any security. In this paper, we consider short-lived MANETs and present a secure, efficient and a fully non-interactive admission control protocol for such networks. More specifically, our work is focused on novel applications of non-interactive secret sharing techniques based on bi-variate polynomials, but, unlike other results, the associated costs are very low.