Embedded devices play a major role in supporting critical infrastructure, but lack many of the security pro-tections of sophisticated systems. Determining whether these devices are compromised is, therefore, a challenge. In this work, we describe a novel control flow verification methodology via electromagnetic (EM) emanations. We design a framework that incorporates signal processing and training to detect subtle control flow deviations as small as three clock cycles, the minimum required to execute a return with malicious activity on modern embedded hardware. Our methodology leverages basic block detection, enabling the discovery of these subtle control flow deviations that escape conventional detection approaches. We evaluate our framework's ability to detect insertion and modification control flow attacks on six different case studies of real-world critical operations and two processors featuring different architectures. Our results show 96.6% detection accuracy across all tested programs and attacks. Finally, we show the transferability of our methodology to different instances of our evaluated processors, reaching up to 98.7% convergence with our baseline models while requiring a third of the collected EM samples compared to standard retraining. In doing so, we reveal the feasibility of fine-grained EM-based control flow monitoring for low-power microcontrollers.

Sharing high-quality research data specifically for reuse in future work helps the scientific community progress by enabling researchers to build upon existing work and explore new research questions without duplicating data collection efforts. Because current discussions about research artifacts in Computer Security focus on reproducibility and availability of source code, the reusability of data is unclear. We examine data sharing practices in Computer Security and Measurement to provide resources and recommendations for sharing reusable data. Our study covers five years (2019–2023) and seven conferences in Computer Security and Measurement, identifying 948 papers that create a dataset as one of their contributions. We analyze the 265 accessible datasets, evaluating their under-standability and level of reuse. Our findings reveal inconsistent practices in data sharing structure and documentation, causing some datasets to not be shared effectively. Additionally, reuse of datasets is low, especially in fields where the nature of the data does not lend itself to reuse. Based on our findings, we offer data-driven recommendations and resources for improving data sharing practices in our community. Furthermore, we encourage authors to be intentional about their data sharing goals and align their sharing strategies with those goals.

In response to the growing sophistication of censorship methods deployed by governments worldwide, the existence of open-source censorship measurement platforms has increased. Analyzing censorship data is challenging due to the data’s large size, diversity, and variability, requiring a comprehensive understanding of the data collection process and applying established data analysis techniques for thorough information extraction. In this work, we develop a framework that is applicable across all major censorship datasets to continually identify changes in censorship data trends and reveal potentially unreported censorship. Our framework consists of control charts and the Mann-Kendall trend detection test, originating from statistical process control theory, and we implement it on Censored Planet, GFWatch, the Open Observatory of Network Interference (OONI), and Tor data from Russia, Myanmar, China, Iran, Türkiye, and Pakistan from January 2021 through March 2023. Our study confirms results from prior studies and also identifies new events that we validate through media reports. Our correlation analysis reveals minimal similarities between censorship datasets. However, because our framework is applicable across all major censorship datasets, it significantly reduces the manual effort required to employ multiple datasets, which we further demonstrate by applying it to four additional Internet outage-related datasets. Our work thus provides a tool for continuously monitoring censorship activity and acts as a basis for developing more systematic, holistic, and in-depth analysis techniques for censorship data.

Cellular network infrastructure serves as the backbone of modern mobile wireless communication. As such, cellular cores must be proactively secured against external threats to ensure reliable service. Compromised base station attacks against the core are a rising threat to cellular networks, while user device inputs have long been considered as an attack vector; despite this, few techniques exist to comprehensively test RAN-Core interfaces against malicious input. In this work, we devise a fuzzing framework that performantly fuzzes cellular interfaces accessible from a base station or user device, overcoming several challenges in fuzzing specific to LTE/5G network components. We also introduce ASNFuzzGen, a tool that compiles ASN.1 specifications into structure-aware fuzzing modules, thereby facilitating effective fuzzing exploration of complex cellular protocols. We run fuzzing campaigns against seven open-source and commercial cores and discover 119 vulnerabilities, with 93 CVEs assigned. Our results reveal common implementation mistakes across several cores that lead to vulnerabilities, and the successful coordination of patches for these vulnerabilities across several vendors demonstrates the practical impact ASNFuzzGen has on hardening user-exposed cellular systems.

Continuous location sharing (CLS) applications are widely used for safety and social convenience. However, these applications have privacy concerns that can be used for control and harm. To understand user concerns, we performed the largest user study of CLS application usage performed to date, with 1500 of 3000 users indicating they use CLS applications and 896 of these users completing surveys. From survey responses, we conducted 23 interviews with participants who had uncomfortable experiences. With these interviews, we perform thematic analysis grounded by sociological frameworks of power dynamics and social exchange theory. We observe that CLS application users face discomfort related to three primary categories that build on each other: (1) overstepped boundaries, (2) continued discomfort, and (3) lifestyle-impacting behaviors. With this foundational understanding, we suggest features that aim to reduce relationship imbalances that CLS applications enable. Our resulting study demonstrates that CLS applications contribute to interpersonal discomfort, highlighting the need for design changes.

Audio deepfakes represent a rising threat to trust in our daily communications. In response to this, the research community has developed a wide array of detection techniques aimed at preventing such attacks from deceiving users. Unfortunately, the creation of these defenses has generally overlooked the most important element of the system - the user themselves. As such, it is not clear whether current mechanisms augment, hinder, or simply contradict human classification of deepfakes. In this paper, we perform the first large-scale user study on deepfake detection. We recruit over 1,200 users and present them with samples from the three most widely-cited deepfake datasets. We then quantitatively compare performance and qualitatively conduct thematic analysis to motivate and understand the reasoning behind user decisions and differences from machine classifications. Our results show that users correctly classify human audio at significantly higher rates than machine learning models, and rely on linguistic features and intuition when performing classification. However, users are also regularly misled by pre-conceptions about the capabilities of generated audio (e.g., that accents and background sounds are indicative of humans). Finally, machine learning models suffer from significantly higher false positive rates, and experience false negatives that humans correctly classify when issues of quality or robotic characteristics are reported. By analyzing user behavior across multiple deepfake datasets, our study demonstrates the need to more tightly compare user and machine learning performance, and to target the latter towards areas where humans are less likely to successfully identify threats.

Industry is increasingly adopting private 5G networks to securely manage their wireless devices in retail, manufacturing, natural resources, and healthcare. As with most technology sectors, open-source software is well poised to form the foundation of deployments, whether it is deployed directly or as part of well-maintained proprietary offerings. This paper seeks to examine the use of cryptography and secure randomness in open-source cellular cores. We design a set of 13 CodeQL static program analysis rules for cores written in both C/C++ and Go and apply them to 7 open-source cellular cores implementing 4G and 5G functionality. We identify two significant security vulnerabilities, including predictable generation of TMSIs and improper verification of TLS certificates, with each vulnerability affecting multiple cores. In identifying these flaws, we hope to correct implementations to fix downstream deployments and derivative proprietary projects.

Reproducibility is crucial to the advancement of science; it strengthens confidence in seemingly contradictory results and expands the boundaries of known discoveries. Computer Security has the natural benefit of creating artifacts that should facilitate computational reproducibility, the ability for others to use someone else's code and data to independently recreate results, in a relatively straightforward fashion. While the Security community has recently increased its attention on reproducibility, an independent and comprehensive measurement of the current state of reproducibility has not been conducted. In this paper, we perform the first such study, targeting reproducible artifacts generated specifically by papers on machine learning security (one of the most popular areas in academic research) published in Tier 1 security conferences over the past ten years (2013-2022). We perform our measurement study of indirect and direct reproducibility over nearly 750 papers, their codebases, and datasets. Our analysis shows that there is no statistically significant difference between the availability of artifacts before and after the introduction of Artifact Evaluation Committees in Tier 1 conferences. However, based on three years of results, artifacts that pass through this process work at a higher rate than those that do not. From our collected findings, we offer data-driven suggestions for improving reproducibility in our community, including five common problems observed in our study. In so doing, we demonstrate that significant progress still needs to be made in computational reproducibility in Computer Security research.

Audio Rogue Base Stations (ARBSs) allow an adversary to intercept cellular calls. These devices represent a substantial escalation in the threat posed by traditional rogue base stations, which only collect device identity information. This paper presents the first technique for detecting call eavesdropping via an ARBS. Our system, which we call LeopardSeal, uses distance bounding over the call audio channel to determine whether or not extra wireless hops (and therefore increased audio delay) that are characteristic of ARBSs are present during a call. We implement a proof of concept ARBS using open-source guides and perform a measurement study across the United States. We demonstrate the ability to detect all attacks (with zero false positives) due to a statistically significant difference in round trip times between benign and attack call audio (i.e., t-test: p ≪ 0.01) due to the large cost of additional wireless hops. Through this effort, we demonstrate the ability to robustly detect these eavesdropping devices.

Bluetooth is overwhelmingly the protocol of choice for personal area networking, and the Bluetooth Classic standard has been in continuous use for over 20 years. Bluetooth devices make themselves Discoverable to communicate, but best practice to protect privacy is to ensure that devices remain in Non-Discoverable mode. This paper demonstrates the futility of protecting devices by making them Non-Discoverable. We introduce the Blue’s Clues attack, which presents the first direct, non-disruptive approach to fully extracting the permanent, unique Bluetooth MAC identifier from targeted devices in Non-Discoverable mode. We also demonstrate that we can fully characterize device capabilities and retrieve identifiers, some of which we discover often contain identifying information about the device owner. We demonstrate Blue’s Clues using a software-defined radio and mounting the attack over the air against both our own devices and, with institutional approval, throughout a public building. We find that a wide variety of Bluetooth devices can be uniquely identified in less than 10 seconds on average, with affected devices ranging from smartphones and headphones to gas pump skimmers and nanny-cams, spanning all versions of the Bluetooth Classic standard. While we provide potential mitigation against attacks, Blue’s Clues forces a reassessment of over 20 years of best practices for protecting devices against discovery.

SMS-based two-factor authentication (2FA) is the most widely deployed 2FA mechanism, despite the fact that SMS messages are known to be vulnerable to rerouting attacks, and despite the availability of alternatives that may be more secure. This is for two reasons. First, it is very effective in practice, as evidenced by reports from Google and Microsoft. Second, users prefer SMS over alternatives, because text messaging is already part of their daily communication. Accepting this practical reality, we developed a new SMS-based protocol that makes rerouting attacks useless to adversaries who aim to take over user accounts. Our protocol delivers one-time passwords (OTP) via text message in a manner that adds minimal overhead (to both the user and the server) over existing SMS-based methods, and is implemented with only small changes to the stock text-message applications that already ship on mobile phones. The security of our protocol rests upon a provably secure authenticated key exchange protocol that, crucially, does not place significant new burdens upon the user. Indeed, we carry out a user study that demonstrates no statistically significant difference between traditional SMS and our protocol, in terms of usability.

Stalkerware is a form of malware that allows for the abusive monitoring of intimate partners. Primarily deployed on information-rich mobile platforms, these malicious applications allow for collecting information about a victim’s actions and behaviors, including location data, call audio, text messages, photos, and other personal details. While stalkerware has received increased attention from the security community, the ways in which stalkerware authors monetize their efforts have not been explored in depth. This paper represents the first large-scale technical analysis of monetization within the stalkerware ecosystem. We analyze the code base of 6,432 applications collected by the Coalition Against Stalkerware to determine their monetization strategies. We find that while far fewer stalkerware apps use ad libraries than normal apps, 99% of those that do use Google AdMob. We also find that payment services range from traditional in-app billing to cryptocurrency. Finally, we demonstrate that Google’s recent change to their Terms of Service (ToS) did not eliminate these applications, but instead caused a shift to other payment processors, while the apps can still be found on the Play Store; we verify through emulation that these apps often operate in blatant contravention of the ToS. Through this analysis, we find that the heterogeneity of markets and payment processors means that while point solutions can have impact on monetization, a multi-pronged solution involving multiple stakeholders is necessary to mitigate the financial incentive for developing stalkerware.

Automatic Speech Recognition (ASR) systems convert speech into text and can be placed into two broad categories: traditional and fully end-to-end. Both types have been shown to be vulnerable to adversarial audio examples that sound benign to the human ear but force the ASR to produce malicious transcriptions. Of these attacks, only the “psychoacoustic” attacks can create examples with relatively imperceptible perturbations, as they leverage the knowledge of the human auditory system. Unfortunately, existing psychoacoustic attacks can only be applied against traditional models, and are obsolete against the newer, fully end-to-end ASRs. In this paper, we propose an equalization-based psychoacoustic attack that can exploit both traditional and fully end-to-end ASRs. We successfully demonstrate our attack against real-world ASRs that include DeepSpeech and Wav2Letter. Moreover, we employ a user study to verify that our method creates low audible distortion. Specifically, 80 of the 100 participants voted in favor of \textit{all} our attack audio samples as less noisier than the existing state-of-the-art attack. Through this, we demonstrate both types of existing ASR pipelines can be exploited with minimum degradation to attack audio quality.

Automatic speech recognition and voice identification systems are being deployed in a wide array of applications, from providing control mechanisms to devices lacking traditional interfaces, to the automatic transcription of conversations and authentication of users. Many of these applications have significant security and privacy considerations. We develop attacks that force mistranscription and misidentification in state of the art systems, with minimal impact on human comprehension. Processing pipelines for modern systems are comprised of signal preprocessing and feature extraction steps, whose output is fed to a machine-learned model. Prior work has focused on the models, using white-box knowledge to tailor model-specific attacks. We focus on the pipeline stages before the models, which (unlike the models) are quite similar across systems. As such, our attacks are black-box, transferable, can be tuned to require zero queries to the target, and demonstrably achieve mistranscription and misidentification rates as high as 100% by modifying only a few frames of audio. We perform a study via Amazon Mechanical Turk demonstrating that there is no statistically significant difference between human perception of regular and perturbed audio. Our findings suggest that models may learn aspects of speech that are generally not perceived by human subjects, but that are crucial for model accuracy.

Speech and speaker recognition systems are employed in a variety of applications, from personal assistants to telephony surveillance and biometric authentication. The wide deployment of these systems has been made possible by the improved accuracy in neural networks. Like other systems based on neural networks, recent research has demonstrated that speech and speaker recognition systems are vulnerable to attacks using manipulated inputs. However, as we demonstrate in this paper, the end-to-end architecture of speech and speaker systems and the nature of their inputs make attacks and defenses against them substantially different than those in the image space. We demonstrate this first by systematizing existing research in this space and providing a taxonomy through which the community can evaluate future work. We then demonstrate experimentally that attacks against these models almost universally fail to transfer. In so doing, we argue that substantial additional work is required to provide adequate mitigations in this space.

Robocalls interrupt daily activity, cause financial harm, and influence users to ignore calls from unfamiliar numbers. Service providers and developers have created Anti-Robocall applications to attempt to restore trust in the phone and decrease the impact of robocalls on daily life. However, whether or not such applications meet accessibility standards and are therefore usable by vulnerable populations, particularly the visually impaired, is unknown. In this paper, we use a combination of the W3C's Mobile Web Content Accessibility Guidelines (MWCAG) and interviews with 11 visually impaired users to establish accessibility metrics for Anti-Robocall applications. We then evaluate 56 Anti-Robocall applications for Android to assess whether they met the needs of the visually impaired community. Our results indicate that 100% of the applications fail to meet all basic accessibility guidelines including minimum color contrast, button labels (to assist screen readers), and automatic audible alerts. As a result, we show that despite the availability of a variety of tools to help developers identify and correct these problems, this important class of applications does not meet basic accessibility requirements. We conclude by suggesting viable paths forward that ensure inclusion and protection for the visually impaired community.

A protocol for two-party secure function evaluation (2P-SFE) aims to allow the parties to learn the output of function f of their private inputs, while leaking nothing more. In a sense, such a protocol realizes a trusted oracle that computes f and returns the result to both parties. There have been tremendous strides in efficiency over the past ten years, yet 2P-SFE protocols remain impractical for most real-time, online computations, particularly on modestly provisioned devices. Intel's Software Guard Extensions (SGX) provides hardware-protected execution environments, called enclaves, that may be viewed as trusted computation oracles. While SGX provides native CPU speed for secure computation, previous side-channel and micro-architecture attacks have demonstrated how security guarantees of enclaves can be compromised. In this paper, we explore a balanced approach to 2P-SFE on SGX-enabled processors by constructing a protocol for evaluating f relative to a partitioning of f. This approach alleviates the burden of trust on the enclave by allowing the protocol designer to choose which components should be evaluated within the enclave, and which via standard cryptographic techniques. We describe SGX-enabled SFE protocols (modeling the enclave as an oracle), and formalize the strongest-possible notion of 2P-SFE for our setting. We prove our protocol meets this notion when properly realized. We implement the protocol and apply it to two practical problems: privacy-preserving queries to a database, and a version of Dijkstra's algorithm for privacy-preserving navigation. Our evaluation shows that our SGX-enabled SFE scheme enjoys a 38x increase in performance over garbled-circuit-based SFE. Finally, we justify modeling of the enclave as an oracle by implementing protections against known side-channels.

In July 2018, over a decade after the DES encryption algorithm was retired, 3DES was also officially deprecated. While previous work suggests a successful deprecation of DES, with fewer than 1% of observed SSL/TLS handshakes using some form of DES up until 2018, such work tends to be limited in scope and does not necessarily capture the true persistence of DES across the entire TLS ecosystem. In this paper, we actively investigate online support for DES and DES-derivative ciphers by querying IP addresses responsive to port 443 connection attempts. To achieve this, we design and implement our own Internet scanning tool built upon ZMap and attempt to negotiate handshakes exclusively using DES ciphers. In total, we have scanned over 31 million unique IP addresses and found that nearly half of them can still successfully establish an HTTPS connection using at least one DES cipher. Moreover, we also find that many servers still support DES40 (which can be broken in seconds) and anon ciphers (which offer no certificate verification and are vulnerable to man-in-the-middle attacks). Our investigation demonstrates the biases and misunderstandings in previous weak cipher studies within the TLS ecosystem, and discloses the severity of this problem by targeting DES-based cipher suites.

With close to native performance, Linux containers are becoming the de facto platform for cloud computing. While various solutions have been proposed to secure applications and containers in the cloud environment by leveraging Intel SGX, most cloud operators do not yet offer SGX as a service. This is likely due to a number of security, scalability, and usability concerns coming from both cloud providers and users. Cloud operators worry about the security guarantees of unofficial SDKs, limited support for remote attestation within containers, limited physical memory for the Enclave Page Cache (EPC) making it difficult to support hundreds of enclaves, and potential DoS attacks against EPC by malicious users. Meanwhile, end users need to worry about careful program partitioning to reduce the TCB and adapting legacy applications to use SGX. We note that most of these concerns are the result of an incomplete infrastructure, from the OS to the application layer. We address these concerns with lxcsgx, which allows SGX applications to run inside containers while also: enabling SGX remote attestation for containerized applications, enforcing EPC memory usage control on a per-container basis, providing a general software TPM using SGX to augment legacy applications, and supporting partitioning with a GCC plugin. We then retrofit Nginx/OpenSSL and Memcached using the software TPM and SGX partitioning to defend against known and potential attacks. Thanks to the small EPC footprint of each enclave, we are able to run up to 100 containerized Memcached instances without EPC swapping. Our evaluation shows the overhead introduced by lxcsgx is less than 6.9% for simple SGX applications, 9.5% for Nginx/OpenSSL, and 20.9% for containerized Memcached.

Credit and debit cards enable financial transactions at unattended "pay-at-the-pump" gas station terminals across North America. Attackers discreetly open these pumps and install skimmers, which copy sensitive card data. While EMV (“chip-and-PIN”) has made substantial inroads in traditional retailers, such systems have virtually no deployment at pay-at-the-pump terminals due to dramatically higher costs and logistical/regulatory constraints, leaving consumers vulnerable in these contexts. In an effort to improve security, station owners have deployed security indicators such as low-cost tamper-evident seals, and technologists have developed skimmer detection apps for mobile phones. Not only do these solutions put the onus on consumers to notice and react to security concerns at the pump, but the efficacy of these solutions has not been measured. In this paper, we evaluate the indicators available to consumers to detect skimmers. We perform a comprehensive teardown of all known skimmer detection apps for iOS and Android devices, and then conduct a forensic analysis of real-world gas pump skimmer hardware recovered by multiple law enforcement agencies. Finally, we analyze anti-skimmer mechanisms deployed by pump owners/operators, and augment this investigation with an analysis of skimmer reports and accompanying security measures collected by the Florida Department of Agriculture and Consumer Services over four years, making this the most comprehensive long-term study of such devices. Our results show that common gas pump security indicators are not only ineffective at empowering consumers to detect tampering, but may be providing a false sense of security. Accordingly, stronger, reliable, inexpensive measures must be developed to protect consumers and merchants from fraud.

Access to credit can provide capital crucial to both businesses and individuals. Unfortunately, for large parts of the developing world, access to credit is not available because customers often lack the traditional data used by lenders to make such decisions (e.g., verifiable payroll statements, property ownership documents). Emerging online credit services address this need through the use of non-traditional creditworthiness data, which many believe to include user geolocation and social network information. While such systems both potentially expand credit availability and improve usability through instant evaluation, their security and privacy practices remain opaque. In this paper, we perform the first comprehensive security analysis of the emerging online credit space. To provide improved transparency, we select 51 representative companies across the industry, analyze their privacy policies and compare them to the sensitive data types mobile applications actually gather. We then evaluate the configuration of connections between mobile apps and their supporting servers to determine whether they securely handle such data. Our analysis demonstrates significant security and privacy issues across this burgeoning industry, including the gathering of previously undisclosed data types and widespread mis-configuration of encryption. We conclude by discussing our efforts to work with partners in and around the industry to improve these issues.

In cooperative spectrum sensing, multiple sensors work together to perform tasks such as localizing a target transmitter. However, the exchange of spectrum measurements leads to exposure of the physical location of participating sensors. Furthermore, in some cases, the sensitive characteristics of all participants can be revealed through the compromise of any one sensor. Accordingly, without guarantees about how data will be handled, there is little reason for such devices to work together. In this work, we protect the location of sensors cooperating in spectrum sensing by processing measurements within attestable containers, or enclaves. We use the enclave as a building block for new privacy-preserving particle filter protocols. We instantiate this enclave using Intel Software Guard Extensions (SGX) and investigate how the inclusion of enclaves impacts sensor privacy, carefully enumerating the different threats present in centralized and decentralized architectures. We show that enclave-based particle filter protocols incur minimal overhead (adding 16 milliseconds of processing to the measurement processing function versus unprotected computation), whereas cryptographically-based approaches suffer from multiple orders of magnitude greater costs. Our work demonstrates that enclaves can be effectively deployed in a decentralized architecture while dramatically improving current data handling techniques.

Outsourced secure multiparty computation (SMC) protocols allow resource-constrained devices to execute input-private computation with great efficiency. Unfortunately, existing outsourced SMC protocols require that all parties know the function being evaluated, precluding applications where the function itself must remain private. We develop the first linear-complexity protocols for outsourcing private function evaluation (PFE), SMC protocols that provide input and function privacy. Assuming a semi-honest function holder, we build on existing two-party PFE constructions to develop outsourced protocols that are secure against a semi-honest, covert, or malicious outsourcing server and malicious mobile participants. To do this, we develop a garbling technique for combining public and private sub-circuits in a single computation. This allows us to apply auxiliary checks for malicious behaviour using only free-XOR gates. These protocols demonstrate the feasibility of outsourced PFE and provide a first step towards privacy-preserving applications for use in cloud computing.

Recent years have seen the Short Message Service (SMS) become a critical component of the security infrastructure, assisting with tasks including identity verification and second-factor authentication. At the same time, this messaging infrastructure has become dramatically more open and connected to public networks than ever before. However, the implications of this openness, the security practices of benign services, and the malicious misuse of this ecosystem are not well understood. In this article, we provide a comprehensive longitudinal study to answer these questions, analyzing over 900,000 text messages sent to public online SMS gateways over the course of 28 months. From this data, we uncover the geographical distribution of spam messages, study SMS as a transmission medium of malicious content, and find that changes in benign and malicious behaviors in the SMS ecosystem have been minimal during our collection period. The key takeaways of this research show many services sending sensitive security-based messages through an unencrypted medium, implementing low entropy solutions for one-use codes, and behaviors indicating that public gateways are primarily used for evading account creation policies that require verified phone numbers. This latter finding has significant implications for combating phone-verified account fraud and demonstrates that such evasion will continue to be difficult to detect and prevent.

Android applications are frequently plagiarized or repackaged, and software obfuscation is a recommended protection against these practices. However, there is very little data on the overall rates of app obfuscation, the techniques used, or factors that lead to developers to choose to obfuscate their apps. In this paper, we present the first comprehensive analysis of the use of and challenges to software obfuscation in Android applications. We analyzed 1.7 million free Android apps from Google Play to detect various obfuscation techniques, finding that only 24.92% of apps are obfuscated by the developer. To better understand this rate of obfuscation, we surveyed 308 Google Play developers about their experiences and attitudes about obfuscation. We found that while developers feel that apps in general are at risk of plagiarism, they do not fear theft of their own apps. Developers also report difficulties obfuscating their own apps. To better understand, we conducted a follow-up study where the vast majority of 70 participants failed to obfuscate a realistic sample app even while many mistakenly believed they had been successful. These findings have broad implications both for improving the security of Android apps and for all tools that aim to help developers write more secure software.

Voice controlled interfaces have vastly improved the usability of many devices (e.g., headless IoT systems). Unfortunately, the lack of authentication for these interfaces has also introduced command injection vulnerabilities - whether via compromised IoT devices, television ads or simply malicious nearby neighbors, causing such devices to perform unauthenticated sensitive commands is relatively easy. We address these weaknesses with Two Microphone Authentication (2MA), which takes advantage of the presence of multiple ambient and personal devices operating in the same area. We develop an embodiment of 2MA that combines approximate localization through Direction of Arrival (DOA) techniques with Robust Audio Hashes (RSHs). Our results show that our 2MA system can localize a source to within a narrow physical cone ($<30^\circ $) with zero false positives, eliminate replay attacks and prevent the injection of inaudible/hidden commands. As such, we dramatically increase the difficulty for an adversary to carry out such attacks and demonstrate that 2MA is an effective means of authenticating and localizing voice commands.

Data breaches represent a significant threat to organizations. While the general problem of protecting data has received much attention, one large (and growing) class has not - data that must be kept due to mandatory retention laws. Such data is often of little use to an organization, is rarely accessed, and represents a significant potential liability, yet cannot be discarded. Protecting such data entails an unusual combination of practical constraints (such as providing verification to a party that may be unknown) and thus requires functionality that is not well addressed by traditional cryptographic primitives. We propose to mitigate the risk to such data through a new system called Dragchute, which creates a time window during which locked data cannot be accessed by anyone. Based on a verifiable non-interactive, non-parallelizable, time-delay key escrow mechanism, Dragchute is novel in that it requires that no cryptographic material capable of providing early access to the data be retained, yet provides verification for multiple properties. We define a base construction for Dragchute, show possible extensions that help meet additional verification requirements, and characterize its performance. Our results show that Dragchute systems offer verifiable, customizable, computational protection against data exposure for encryption costs similar to traditional methods (e.g., less than 6% overhead compared to AEAD). We thus show that Dragchute systems provide a critical new means for protecting data that must be retained long term due to mandatory retention laws.

The global telephone network is relied upon by billions every day. Central to its operation is the Signaling System 7 (SS7) protocol, which is used for setting up calls, managing mobility, and facilitating many other network services. This protocol was originally built on the assumption that only a small number of trusted parties would be able to directly communicate with its core infrastructure. As a result, SS7 - as a feature - allows all parties with core access to redirect and intercept calls for any subscriber anywhere in the world. Unfortunately, increased interconnectivity with the SS7 network has led to a growing number of illicit call redirection attacks. We address such attacks with Sonar, a system that detects the presence of SS7 redirection attacks by securely measuring call audio round-trip times between telephony devices. This approach works because redirection attacks force calls to travel longer physical distances than usual, thereby creating longer end-to-end delay. We design and implement a distance bounding-inspired protocol that allows us to securely characterize the round-trip time between the two endpoints. We then use custom hardware deployed in 10 locations across the United States and a redirection testbed to characterize how distance affects round trip time in phone networks. We develop a model using this testbed and show Sonar is able to detect 70.9% of redirected calls between call endpoints of varying attacker proximity (300-7100 miles) with low false positive rates (0.3%). Finally, we ethically perform actual SS7 redirection attacks on our own devices with the help of an industry partner to demonstrate that Sonar detects 100% of such redirections in a real network (with no false positives). As such, we demonstrate that telephone users can reliably detect SS7 redirection attacks and protect the integrity of their calls.

Gift cards are an increasingly popular payment platform. Much like credit cards, gift cards rely on a magnetic stripe to encode account information. Unlike credit cards, however, the EMV standard is entirely infeasible for gift cards due to compatibility and cost. As such, much of the fraud that has plagued credit cards has started to move towards gift cards, resulting in billions of dollars of loss annually. In this paper, we present a system for detecting counterfeit magnetic stripe gift cards that does not require the original card to be measured at the time of manufacture. Our system relies on a phenomenon known as jitter, which is present on all ISO/IEC-standard magnetic stripe cards. Variances in bit length are induced by the card encoding hardware and are difficult and expensive to reduce. We verify this hypothesis with a high-resolution magneto-optical microscope, then build our detector using inexpensive, commodity card readers. We then partnered with Walmart to evaluate their gift cards and distinguished legitimate gift cards from our clones with up to 99.3% accuracy. Our results show that measurement and detection of jitter increases the difficulty for adversaries to produce undetectable counterfeits, thereby creating significant opportunity to reduce gift card fraud.

Voice interfaces are increasingly becoming integrated into a variety of Internet of Things (IoT) devices. Such systems can dramatically simplify interactions between users and devices with limited displays. Unfortunately voice interfaces also create new opportunities for exploitation. Specifically any sound-emitting device within range of the system implementing the voice interface (e.g., a smart television, an Internet-connected appliance, etc) can potentially cause these systems to perform operations against the desires of their owners (e.g., unlock doors, make unauthorized purchases, etc). We address this problem by developing a technique to recognize fundamental differences in audio created by humans and electronic speakers. We identify sub-bass over-excitation, or the presence of significant low frequency signals that are outside of the range of human voices but inherent to the design of modern speakers, as a strong differentiator between these two sources. After identifying this phenomenon, we demonstrate its use in preventing adversarial requests, replayed audio, and hidden commands with a 100%/1.72% TPR/FPR in quiet environments. In so doing, we demonstrate that commands injected via nearby audio devices can be effectively removed by voice interfaces.

Abstract The majority of people across the globe rely on telephony networks as their primary means of communication. As such, many of the most sensitive personal, corporate and government related communications pass through these systems every day. Unsurprisingly, such connections are subject to a wide range of attacks. Of increasing concern is the use of metadata contained in Call Detail Records (CDRs), which contain source, destination, start time and duration of a call. This information is potentially dangerous as the very act of two parties communicating can reveal significant details about their relationship and put them in the focus of targeted observation or surveillance, which is highly critical especially for journalists and activists. To address this problem, we develop the Phonion architecture to frustrate such attacks by separating call setup functions from call delivery. Specifically, Phonion allows users to preemptively establish call circuits across multiple providers and technologies before dialing into the circuit and does not require constant Internet connectivity. Since no single carrier can determine the ultimate destination of the call, it provides unlinkability for its users and helps them to avoid passive surveillance. We define and discuss a range of adversary classes and analyze why current obfuscation technologies fail to protect users against such metadata attacks. In our extensive evaluation we further analyze advanced anonymity technologies (e.g., VoIP over Tor), which do not preserve our functional requirements for high voice quality in the absence of constant broadband Internet connectivity and compatibility with landline and feature phones. Phonion is the first practical system to provide guarantees of unlinkable communication against a range of practical adversaries in telephony systems.

Mobile money, also known as branchless banking, leverages ubiquitous cellular networks to bring much-needed financial services to the unbanked in the developing world. These services are often deployed as smartphone apps, and although marketed as secure, these applications are often not regulated as strictly as traditional banks, leaving doubt about the truth of such claims. In this article, we evaluate these claims and perform the first in-depth measurement analysis of branchless banking applications. We first perform an automated analysis of all 46 known Android mobile money apps across the 246 known mobile money providers from 2015. We then perform a comprehensive manual teardown of the registration, login, and transaction procedures of a diverse 15% of these apps. We uncover pervasive vulnerabilities spanning botched certification validation, do-it-yourself cryptography, and other forms of information leakage that allow an attacker to impersonate legitimate users, modify transactions, and steal financial records. These findings show that the majority of these apps fail to provide the protections needed by financial services. In an expanded re-evaluation one year later, we find that these systems have only marginally improved their security. Additionally, we document our experiences working in this sector for future researchers and provide recommendations to improve the security of this critical ecosystem. Finally, through inspection of providers’ terms of service, we also discover that liability for these problems unfairly rests on the shoulders of the customer, threatening to erode trust in branchless banking and hinder efforts for global financial inclusion.

Mobile specific webpages differ significantly from their desktop counterparts in content, layout, and functionality. Accordingly, existing techniques to detect malicious websites are unlikely to work for such webpages. In this paper, we design and implement kAYO, a mechanism that distinguishes between malicious and benign mobile webpages. kAYO makes this determination based on static features of a webpage ranging from the number of iframes to the presence of known fraudulent phone numbers. First, we experimentally demonstrate the need for mobile specific techniques and then identify a range of new static features that highly correlate with mobile malicious webpages. We then apply kAYO to a dataset of over 350,000 known benign and malicious mobile webpages and demonstrate 90 percent accuracy in classification. Moreover, we discover, characterize, and report a number of webpages missed by Google Safe Browsing and VirusTotal, but detected by kAYO. Finally, we build a browser extension using kAYO to protect users from malicious mobile websites in real-time. In doing so, we provide the first static analysis technique to detect malicious mobile webpages.

Recent developments in secure computation have led to significant improvementsin efficiency and functionality. These efforts created compilers that form thebackbone of practical secure computation research. Unfortunately, many of theartifacts that are being used to demonstrate new research for secure computationare incomplete, incorrect, or unstable, leading to demonstrably erroneousresults and inefficiencies - extending even to the most recently developedcompiler systems. This is a problem because it hampers research and underminesfeasibility tests when other researchers attempt to use these tools. We addressthese problems and present Frigate, a principled compiler and fast circuitinterpreter for secure computation. To ensure correctness we apply bestpractices for compiler design and development, including the use of standarddata structures, helpful negative results, and structured validation testing. Our systematic validation tests include checks on the internal compiler state, combinations of operators, and edge cases based on widely used techniques anderrors we have observed in other work. This produces a compiler that buildscorrect circuits, is efficient and extensible. Frigate creates circuits withgate counts comparable to previous work, but does so with compile time speedupsas high as 447x compared with the best results from previous work in circuit compilers. By creating avalidated tool, our compiler will allow future secure computationimplementations to be developed quickly and correctly.

Ransomware is a growing threat that encrypts auser's files and holds the decryption key until a ransom ispaid by the victim. This type of malware is responsible fortens of millions of dollars in extortion annually. Worse still, developing new variants is trivial, facilitating the evasion of manyantivirus and intrusion detection systems. In this work, we presentCryptoDrop, an early-warning detection system that alerts a userduring suspicious file activity. Using a set of behavior indicators, CryptoDrop can halt a process that appears to be tampering witha large amount of the user's data. Furthermore, by combininga set of indicators common to ransomware, the system can beparameterized for rapid detection with low false positives. Ourexperimental analysis of CryptoDrop stops ransomware fromexecuting with a median loss of only 10 files (out of nearly5,100 available files). Our results show that careful analysis ofransomware behavior can produce an effective detection systemthat significantly mitigates the amount of victim data loss.

Text messages sent via the Short Message Service (SMS) have revolutionized interpersonal communication. Recent years have also seen this service become a critical component of the security infrastructure, assisting with tasks including identity verification and second-factor authentication. At the same time, this messaging infrastructure has become dramatically more open and connected to public networks than ever before. However, the implications of this openness, the security practices of benign services, and the malicious misuse of this ecosystem are not well understood. In this paper, we provide the first longitudinal study to answer these questions, analyzing nearly 400,000 text messages sent to public online SMS gateways over the course of 14 months. From this data, we are able to identify not only a range of services sending extremely sensitive plaintext data and implementing low entropy solutions for one-use codes, but also offer insights into the prevalence of SMS spam and behaviors indicating that public gateways are primarily used for evading account creation policies that require verified phone numbers. This latter finding has significant implications for research combatting phone-verified account fraud and demonstrates that such evasion will continue to be difficult to detect and prevent.

Text messaging is used by more people around the world than any other communications technology. As such, it presents a desirable medium for spammers. While this problem has been studied by many researchers over the years, the recent increase in legitimate bulk traffic (e.g., account verification, 2FA, etc.) has dramatically changed the mix of traffic seen in this space, reducing the effectiveness of previous spam classification efforts. This paper demonstrates the performance degradation of those detectors when used on a large-scale corpus of text messages containing both bulk and spam messages. Against our labeled dataset of text messages collected over 14 months, the precision and recall of past classifiers fall to 23.8% and 61.3% respectively. However, using our classification techniques and labeled clusters, precision and recall rise to 100% and 96.8%. We not only show that our collected dataset helps to correct many of the overtraining errors seen in previous studies, but also present insights into a number of current SMS spam campaigns.

Garbled circuits provide a powerful tool for jointly evaluating functions while preserving the privacy of each user’s inputs. While recent research has made the use of this primitive more practical, such solutions generally assume that participants are symmetrically provisioned with massive computing resources. In reality, most people on the planet only have access to the comparatively sparse computational resources associated with their mobile phones, and those willing and able to pay for access to public cloud computing infrastructure cannot be assured that their data will remain unexposed. We address this problem by creating a new SFE protocol that allows mobile devices to securely outsource the majority of computation required to evaluate a garbled circuit. Our protocol, which builds on the most efficient garbled circuit evaluation techniques, includes a new outsourced oblivious transfer primitive that requires significantly less bandwidth and computation than standard OT primitives and outsourced input validation techniques that force the cloud to prove that it is executing all protocols correctly. After showing that our extensions are secure in the malicious model, we conduct an extensive performance evaluation for a number of standard SFE test applications as well as a privacy-preserving navigation application designed specifically for the mobile use-case. Our system reduces execution time by 98.92% and bandwidth by 99.95% for the edit distance problem of size 128 compared to non-outsourced evaluation. These results show that even the least capable devices are capable of using large garbled circuits for secure computation.

Abstract Secure multiparty computation (SMC) offers a technique to preserve functionality and data privacy in mobile applications. Current protocols that make this costly cryptographic construction feasible on mobile devices securely outsource the bulk of the computation to a cloud provider. However, these outsourcing techniques are built on specific secure computation assumptions and tools, and applying new SMC ideas to the outsourced setting requires the protocols to be completely rebuilt and proven secure. In this work, we develop a generic technique for lifting any secure two‐party computation protocol into an outsourced two‐party SMC protocol. By augmenting the function being evaluated with auxiliary consistency checks and input values, we can create an outsourced protocol with low overhead cost. Our implementation and evaluation show that in the best case our outsourcing additions execute within the confidence intervals of two servers running the same computation and consume approximately the same bandwidth. In addition, the mobile device itself uses minimal bandwidth over a single round of communication. This work demonstrates that efficient outsourcing is possible with any underlying SMC scheme and provides an outsourcing protocol that is efficient and directly applicable to current and future SMC techniques. Copyright © 2016 John Wiley & Sons, Ltd.

Use-after-free conditions occur when an execution path of a process accesses an incorrectly deal located object. Such access is problematic because it may potentially allow for the execution of arbitrary code by an adversary. However, while increasingly common, such flaws are rarely detected by compilers in even the most obvious instances. In this paper, we design and implement a static analysis method for the detection of use-after-free conditions in binary code. Our new analysis is similar to available expression analysis and traverses all code paths to ensure that every object is defined before each use. Failure to achieve this property indicates that an object is improperly freed and potentially vulnerable to compromise. After discussing the details of our algorithm, we implement a tool and run it against a set of enterprise-grade, publicly available binaries. We show that our tool can not only catch textbook and recently released in-situ examples of this flaw, but that it has also identified 127 additional use-after-free conditions in a search of 652 compiled binaries in the Windows system32 directory. In so doing, we demonstrate not only the power of this approach in combating this increasingly common vulnerability, but also the ability to identify such problems in software for which the source code is not necessarily publicly available.

Abstract In many democratic countries, Communications Assistance for Law Enforcement Act (CALEA) wiretaps are used by law enforcement agencies to perform investigations and gather evidence for legal procedures. However, existing CALEA wiretap implementations are often engineered with the assumption that wiretap operators are trustworthy and wiretap targets do not attempt to evade the wiretap. Although it may be possible to construct more robust wiretap architectures by reengineering significant portions of the telecommunications infrastructure, such efforts are prohibitively costly. This paper instead proposes a lightweight accountable wiretapping system for enabling secure audits of existing CALEA wiretapping systems. Our proposed system maintains a tamper-evident encrypted log over wiretap events, enforces access controls over wiretap records, and enables privacy-preserving aggregate queries and compliance checks. We demonstrate using campus-wide telephone trace data from a large university that our approach provides efficient auditing functionalities while incurring only modest overhead. Based on publicly available wiretap reporting statistics, we conservatively estimate that our architecture can support tamper-evident logging for all of the United States’ ongoing CALEA wiretaps using three commodity PCs.

Mobile browsers are increasingly being relied upon to perform security sensitive operations. Like their desktop counterparts, these applications can enable SSL/TLS to provide strong security guarantees for communications over the web. However, the drastic reduction in screen size and the accompanying reorganization of screen real-estate significantly changes the use and consistency of the security indicators and certificate information that alert users of site identity and the presence of strong cryptographic algorithms. In this paper, we perform the first measurement of the state of critical security indicators in mobile browsers. We evaluate ten mobile and two tablet browsers, representing over 90% of the market share, against the recommended guidelines for web user interface to convey security set forth by the World Wide Web Consortium (W3C). While desktop browsers follow the majority of guidelines, our analysis shows that mobile browsers fall significantly short. We also observe notable inconsistencies across mobile browsers when such mechanisms actually are implemented. We show where and how these failures on mobile browsers eliminate clues previously designed for, and still present in, desktop browsers to detect attacks such as phishing and man-in-the-middle. Finally, we offer advice on where current standards are unclear or incomplete.

Garbled circuits offer a powerful primitive for computation on a user's personal data while keeping that data private. Despite recent improvements, constructing and evaluating circuits of any useful size remains expensive on the limited hardware resources of a smartphone, the primary computational device available to most users around the world. In this work, we develop a new technique for securely outsourcing the generation of garbled circuits to a Cloud provider. By outsourcing the circuit generation, we are able to eliminate the most costly operations from the mobile device, including oblivious transfers. Our proofs of security show that this technique provides the best security guarantees of any existing garbled circuit outsourcing protocol. We also experimentally demonstrate that our new protocol, on average, decreases execution time by 75% and reduces network costs by 60% compared to previous outsourcing protocols. In so doing, we demonstrate that the use of garbled circuits on mobile devices can be made nearly as practical as it is becoming for server-class machines.

Mobile applications increasingly require users to surrender private information, such as GPS location or social networking data. To facilitate user privacy when using these applications, secure function evaluation SFE could be used to obliviously compute functions over encrypted inputs. The dominant construction for desktop applications is the Yao garbled circuit, but this technique requires significant processing power and network overhead, making it extremely expensive on resource-constrained mobile devices. In this work, we develop Efficient Mobile Oblivious Computation, a set of SFE protocols customized for the mobile platform. Using partially homomorphic cryptosystems, we develop protocols to meet the needs of two popular application types: location-based and social networking. Using these applications as comparison benchmarks, we demonstrate execution time improvements of 99% and network overhead improvements of 96% over the most optimized garbled circuit techniques. These results show that our protocols provide mobile application developers with a more practical and equally secure alternative to garbled circuits. Copyright © 2013 John Wiley & Sons, Ltd.

The perceived end-to-end performance of the mobile Internet can be impacted by multiple factors including websites, devices, and network components. Constant changes in these factors and network complexity make identifying root causes of high latency difficult. In this paper, we propose a multidimensional diagnosis technique using passive IP flow data collected at ISPs for investigating factors that impact the performance of the mobile Internet. We implement and evaluate our technique over four days of data from a major US cellular provider's network. Our approach identifies several combinations of factors affecting performance. We investigate four combinations indepth to confirm the latency causes chosen by our technique. Our findings include a popular gaming website showing poor performance on a specific device type for over 50% of the flows and web browser traffic on older devices accounting for 99% of poorly performing traffic. Our technique can direct operators in choosing factors having high impact on latency in the mobile Internet.

Malware is a pressing concern for mobile application market operators. While current mitigation techniques are keeping pace with the relatively infrequent presence of malicious code, the rapidly increasing rate of application development makes manual and resource-intensive automated analysis costly at market-scale. To address this resource imbalance, we present the Mobile Application Security Triage (MAST) architecture, a tool that helps to direct scarce malware analysis resources towards the applications with the greatest potential to exhibit malicious behavior. MAST analyzes attributes extracted from just the application package using Multiple Correspondence Analysis (MCA), a statistical method that measures the correlation between multiple categorical (i.e., qualitative) data. We train MAST using over 15,000 applications from Google Play and a dataset of 732 known-malicious applications. We then use MAST to perform triage on three third-party markets of different size and malware composition---36,710 applications in total. Our experiments show that MAST is both effective and performant. Using MAST ordered ranking, malware-analysis tools can find 95% of malware at the cost of analyzing 13% of the non-malicious applications on average across multiple markets, and MAST triage processes markets in less than a quarter of the time required to perform signature detection. More importantly, we show that successful triage can dramatically reduce the costs of removing malicious applications from markets.

The security guarantees provided by SSL/TLS depend on the correct authentication of servers through certificates signed by a trusted authority. However, as recent incidents have demonstrated, trust in these authorities is not well placed. Increasingly, certificate authorities (by coercion or compromise) have been creating forged certificates for a range of adversaries, allowing seemingly secure communications to be intercepted via man-in-the-middle (MITM) attacks. A variety of solutions have been proposed, but their complexity and deployment costs have hindered their adoption. In this paper, we propose Direct Validation of Certificates (DVCert), a novel protocol that, instead of relying on third-parties for certificate validation, allows domains to directly and securely vouch for their certificates using previously established user authentication credentials. By relying on a robust cryptographic construction, this relatively simple means of enhancing server identity validation is not only efficient and comparatively easy to deploy, but it also solves other limitations of third-party solutions. Our extensive experimental analysis in both desktop and mobile platforms shows that DVCert transactions require little computation time on the server (e.g., less than 1 ms) and are unlikely to degrade server performance or user experience. In short, we provide a robust and practical mechanism to enhance server authentication and protect web applications from MITM attacks against SSL/TLS. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Cellular text messaging services are increasingly being relied upon to disseminate critical information during emergencies. Accordingly, a wide range of organizations including colleges and universities now partner with third-party providers that promise to improve physical security by rapidly delivering such messages. Unfortunately, these products do not work as advertised due to limitations of cellular infrastructure and therefore provide a false sense of security to their users. In this paper, we perform the first extensive investigation and characterization of the limitations of an Emergency Alert System (EAS) using text messages as a security incident response mechanism. We show emergency alert systems built on text messaging not only can meet the 10 minute delivery requirement mandated by the WARN Act, but also potentially cause other voice and SMS traffic to be blocked at rates upward of 80 percent. We then show that our results are representative of reality by comparing them to a number of documented but not previously understood failures. Finally, we analyze a targeted messaging mechanism as a means of efficiently using currently deployed infrastructure and third-party EAS. In so doing, we demonstrate that this increasingly deployed security infrastructure does not achieve its stated requirements for large populations.

HTTP cookies are the de facto mechanism for session authentication in Web applications. However, their inherent security weaknesses allow attacks against the integrity of Web sessions. HTTPS is often recommended to protect cookies, but deploying full HTTPS support can be challenging due to performance and financial concerns, especially for highly distributed applications. Moreover, cookies can be exposed in a variety of ways even when HTTPS is enabled. In this article, we propose one-time cookies (OTC), a more robust alternative for session authentication. OTC prevents attacks such as session hijacking by signing each user request with a session secret securely stored in the browser. Unlike other proposed solutions, OTC does not require expensive state synchronization in the Web application, making it easily deployable in highly distributed systems. We implemented OTC as a plug-in for the popular WordPress platform and as an extension for Firefox and Firefox for mobile browsers. Our extensive experimental analysis shows that OTC introduces a latency of less than 6 ms when compared to cookies—a negligible overhead for most Web applications. Moreover, we show that OTC can be combined with HTTPS to effectively add another layer of security to Web applications. In so doing, we demonstrate that one-time cookies can significantly improve the security of Web applications with minimal impact on performance and scalability.

Mobile application markets currently serve as the main line of defense against malicious applications. While marketplace revocations have successfully removed the few overtly malicious applications installed on mobile devices, the anticipated coming flood of mobile malware mandates the need for mechanisms that can respond faster than manual intervention. In this paper, we propose an infrastructure that automatically identifies and responds to malicious mobile applications based on their network behavior. We design and implement a prototype, Airmid, that uses cooperation between in-network sensors and smart devices to identify the provenance of malicious traffic. We then develop sample malicious mobile applications exceeding the capabilities of malware recently discovered in the wild, demonstrate the ease with which they can evade current detection techniques, and then use Airmid to show a range of automated recovery responses ranging from on-device firewalling to application removal.

Anonymous communications systems generally trade off performance for strong cryptographic guarantees of privacy. However, a number of applications with moderate performance requirements (e.g., chat) may require both properties. In this paper, we develop a new architecture that provides provably unlinkable and efficient communications using a single intermediary node. Nodes participating in these Mix-In-Place Networks (MIPNets) exchange messages through a mailbox in an Oblivious Proxy (OP). Clients leverage Secure Function Evaluation (SFE) to send and receive their messages from the OP while blindly but reversibly modifying the appearance of all other messages (i.e., mixing in place) in the mailbox. While an Oblivious Proxy will know that a client participated in exchanges, it can not be certain which, if any, messages that client transmitted or received. We implement and measure our proposed design using a modified version of Fairplay and note reductions in execution times of greater than 98% over the naïve application of garbled circuits. We then develop a chat application on top of the MIPNet architecture and demonstrate its practical use for as many as 100 concurrent users. Our results demonstrate the potential to use SFE-enabled "mixing" in a single proxy as a means of providing provable deniability for applications with near real-time performance requirements.

Mobile phones are increasingly equipped with a range of highly responsive sensors. From cameras and GPS receivers to three-axis accelerometers, applications running on these devices are able to experience rich interactions with their environment. Unfortunately, some applications may be able to use such sensors to monitor their surroundings in unintended ways. In this paper, we demonstrate that an application with access to accelerometer readings on a modern mobile phone can use such information to recover text entered on a nearby keyboard. Note that unlike previous emanation recovery papers, the accelerometers on such devices sample at near the Nyquist rate, making previous techniques unworkable. Our application instead detects and decodes keystrokes by measuring the relative physical position and distance between each vibration. We then match abstracted words against candidate dictionaries and record word recovery rates as high as 80%. In so doing, we demonstrate the potential to recover significant information from the vicinity of a mobile device without gaining access to resources generally considered to be the most likely sources of leakage (e.g., microphone, camera).

Abstract Mobile phones have evolved from simple voice terminals into highly‐capable, general‐purpose computing platforms. While people are becoming increasingly more dependent on such devices to perform sensitive operations, protect secret data, and be available for emergency use, it is clear that phone operating systems are not ready to become mission‐critical systems. Through a pair of vulnerabilities and a simulated attack on a cellular network, we demonstrate that there are a myriad of unmanaged mechanisms on mobile phones, and that control of these mechanisms is vital to achieving reliable use. Through such vectors, mobile phones introduce a variety of new threats to their own applications and the telecommunications infrastructure itself. In this paper, we examine the requirements for providing effective mediation and access control for mobile phones. We then discuss the convergence of cellular networks with the Internet and its impact on effective resource management and quality of service. Based on these results, we argue for user devices that enable predictable behavior in a network—where their trusted computing bases can protect key applications and create predictable network impact. Copyright © 2010 John Wiley & Sons, Ltd.

The performance of SIP proxies is critical for the robust operation of many applications. However, the use of even light-weight authentication schemes can significantly degrade throughput in these systems. In particular, systems in which multiple proxies share a remote authentication database can experience reduced performance due to latency. In this paper, we investigate how the application of parallel execution and batching can be used to maximize throughput while carefully balancing demands for bandwidth and call failure rates. Through the use of a modified version of OpenSER, a high-performance SIP proxy, we demonstrate that the traditional recommendation of simply launching a large number of parallel processes not only incurs substantial overhead and increases dropped calls, but can actually decrease call throughput. An alternative technique that we implement, request batching, similarly fails to achieve high proxy throughput. Through a carefully selected mix of batching and parallelization, we reduce the bandwidth required to maximize authenticated signaling throughput by the proxy by more than 75 percent. This mix also keeps the call loss rates below 1 percent at peak performance. In addition, we demonstrate that the delay introduced by batching is acceptable for VoIP applications. As a result, our technique significantly reduce the cost and increase the throughput of authentication for large-scale networks supporting SIP applications.

The recent diversification of telephony infrastructure allows users to communicate through landlines, mobile phones and VoIP phones. However, call metadata such as Caller-ID is either not transferred or transferred without verification across these networks, allowing attackers to maliciously alter it. In this paper, we develop PinDr0p, a mechanism to assist users in determining call provenance - the source and the path taken by a call. Our techniques detect and measure single-ended audio features to identify all of the applied voice codecs, calculate packet loss and noise profiles, while remaining agnostic to characteristics of the speaker's voice (as this may legitimately change when interacting with a large organization). In the absence of verifiable call metadata, these features in combination with machine learning allow us to determine the traversal of a call through as many as three different providers (e.g., cellular, then VoIP, then PSTN and all combinations and subsets thereof) with 91.6% accuracy. Moreover, we show that once we identify and characterize the networks traversed, we can create detailed fingerprints for a call source. Using these fingerprints we show that we are able to distinguish between calls made using specific PSTN, cellular, Vonage, Skype and other hard and soft phones from locations across the world with over 90% accuracy. In so doing, we provide a first step in accurately determining the provenance of a call.

Proof of a user's identity is not always a sufficient means for making an authorization decision. In an increasing set of circumstances, knowledge of physical location provides additional and necessary context for making decisions about resource access. For example, sensitive information stored on a laptop (e.g. customer records, social security numbers, etc), may require additional protections if a user operates outside of an approved area. However, current localization techniques based on signal strength reporting or specialized hardware fail to achieve this goal. In this paper, we design, develop, deploy and measure a system which securely determines the location of a user to within one meter through using only off-the-shelf 802.11 and Bluetooth equipment. We apply this equipment in a two-phased challenge- response protocol: first determining the general area of the client in the Regionalization phase and then pinpointing it in the Localization phase. Using nearly 32,000 data points collected over 75 days, we argue that the stability of wireless networks over time creates easily distinguishable location profiles by which a client can be positioned. Additionally, we demonstrate the inherent ability of a two-phased protocol to discern a client's location information at a level of granularity no finer than is necessitated by policy. After discussing a number of applications, we build a location-based access control framework that automatically protects a white-listed set of resources through encryption when the user leaves specified areas. Our analyses show that this system provides a realistic and efficient means of incorporating unforgeable location information at the appropriate level of granularity into many authorization decisions.

Attributes define, classify, or annotate the datum to which they are assigned. However, traditional attribute architectures and cryptosystems are ill-equipped to provide security in the face of diverse access requirements and environments. In this paper, we introduce a novel secure information management architecture based on emerging attribute-based encryption (ABE) primitives. A policy system that meets the needs of complex policies is defined and illustrated. Based on the needs of those policies, we propose cryptographic optimizations that vastly improve enforcement efficiency. We further explore the use of such policies in two proposed applications: a HIPAA compliant distributed file system and a social network. A performance analysis and characterization of ABE primitives demonstrates the ability to reduce cryptographic costs by as much as 98% over previously proposed constructions. Through this, we demonstrate that our attribute system is an efficient solution for securely managing information in large, loosely-coupled, distributed systems.

Abstract Densely populated areas are increasingly filled with vulnerable wireless routers set up by unsophisticated users. In isolation, such routers appear to represent only a minor threat, but in aggregate, the threat can be much greater. We introduce the notion of malnets: networks of adversary‐controlled wireless routers targeted to a physical geography. Similar to Internet worms such as Slammer and Code‐Red, malnets are created by the recursive compromise of targeted devices. However, unlike their traditionally wired counterparts, malnet worms exploit only other routers that are within their transmission range. The malnet thus creates a parallel wireless infrastructure that is (a) completely under control of the adversary, and (b) spans a targeted physical area, creating a valuable infrastructure for a variety of virtual and physical attacks. We initially study the propagation characteristics of commercial routers and model inter‐router connectivity using publicly available war‐driving data. The resulting characterization is applied to well‐known epidemiological models to explore the success rates and speeds of malnet creation across cities such as New York, Atlanta, and Los Angles. Finally, we use a sampling of available exploits to demonstrate the construction of multi‐vector, multi‐platform worms capable of targeting wireless routers. Our analysis show that an adversary can potentially deploy a malnet of over 24,000 routers in Manhattan in less than 2,h. Through this work we show that malnets are not only feasible but can be efficiently deployed. Copyright © 2009 John Wiley & Sons, Ltd.

The relationship between physical security and critical infrastructure has traditionally been unidirectional - the former being necessary to sustain the latter. However, certain pieces of critical infrastructure hold the potential to significantly improve the security of individuals and their most sensitive information. In this paper, we develop a pair of mechanisms for cellular networks and mobile devices that augment the physical security of their users' financial credentials. In particular, we create FrauVent, a multi-modal protocol that provides users with information related to a pending questionable transaction (e.g., transaction value, location, vendor) in a way that improves the available context for approving or rejecting such exchanges. Through protocol design, formal verification and implementation of an application for the Android platform, we develop a robust tool to help reduce the costs of fraud without requiring financial institutions to significantly change their extensively deployed end systems (i.e., card readers). More critically, we provide a general framework that allows cellular infrastructure to actively improve the physical security of sensitive information.

Kernel-mode rootkits hide objects such as processes and threads using a technique known as Direct Kernel Object Manipulation (DKOM). Many forensic analysis tools attempt to detect these hidden objects by scanning kernel memory with handmade signatures; however, such signatures are brittle and rely on non-essential features of these data structures, making them easy to evade. In this paper, we present an automated mechanism for generating signatures for kernel data structures and show that these signatures are robust: attempts to evade the signature by modifying the structure contents will cause the OS to consider the object invalid. Using dynamic analysis, we profile the target data structure to determine commonly used fields, and we then fuzz those fields to determine which are essential to the correct operation of the OS. These fields form the basis of a signature for the data structure. In our experiments, our new signature matched the accuracy of existing scanners for traditional malware and found processes hidden with our prototype rootkit that all current signatures missed. Our techniques significantly increase the difficulty of hiding objects from signature scanning.

The vast expansion of interconnectivity with the Internet and the rapid evolution of highly-capable but largely insecure mobile devices threatens cellular networks. In this paper, we characterize the impact of the large scale compromise and coordination of mobile phones in attacks against the core of these networks. Through a combination of measurement, simulation and analysis, we demonstrate the ability of a botnet composed of as few as 11,750 compromised mobile phones to degrade service to area-code sized regions by 93%. As such attacks are accomplished through the execution of network service requests and not a constant stream of phone calls, users are unlikely to be aware of their occurrence. We then investigate a number of significant network bottlenecks, their impact on the density of compromised nodes per base station and how they can be avoided. We conclude by discussing a number of countermeasures that may help to partially mitigate the threats posed by such attacks.

Structured peer-to-peer (P2P) systems have grown enormously because of their scalability, efficiency, and reliability. These systems assign a unique identifier to each user and object. However, current assignment schemes allow an adversary to carefully select user IDs and/or simultaneously obtain many pseudo-identities-ultimately leading to an ability to disrupt the P2P system in very targeted and dangerous ways. In this paper, we propose novel ID assignment protocols based on identity-based cryptography. This approach permits the acquisition of node IDs to be tightly regulated without many of the complexities and costs associated with traditional certificate solutions. We broadly consider the security requirements of ID assignment and present three protocols representing distinct threat and trust models. A detailed empirical study of the protocols is given. Our analysis shows that the cost of our identity-based protocols is nominal, and that the associated identity services can scale to millions of users using a limited number of servers.

Cellular networks are a critical component of the economic and social infrastructures in which we live. In addition to voice services, these networks deliver alphanumeric text messages to the vast majority of wireless subscribers. To encourage the expansion of this new service, telecommunications c ompanies offer connections between their networks and the Internet. The ramifications of such connections, however, have not been fully recognized. In this paper, we evaluate the security impact of the SMS interface on the availability of the cellular phone network. Specifically, we describe the ability to deny voice service to cities the size of Washington DC and Manhattan with little more than a cable modem. Moreover, attacks targeting the entire United States are feasible with resources available to medium-sized zombie networks. This analysis begins with an exploration of the structure of cellular networks. We then characterize network behavior and explore a number of reconnaissance techniques aimed at effectively targeting attacks on these systems. We conclude by discussing countermeasures that mitigate or eliminate the threats introduced by these attacks.

Determining whether a user or system is exercising appropriate security practices is difficult in any context. Such difficulties are particularly pronounced when uncontrolled or unknown platforms join public networks. Commonly practiced techniques used to vet these hosts, such as system scans, have the potential to infringe on the privacy of users. In this article, we show that it is possible for clients to prove both the presence and proper functioning of security infrastructure without allowing unrestricted access to their system. We demonstrate this approach, specifically applied to antivirus security, by requiring clients seeking admission to a network to positively identify the presence or absence of malcode in a series of puzzles. The implementation of this mechanism and its application to real networks are also explored. In so doing, we demonstrate that it is not necessary for an administrator to be invasive to determine whether a client implements required security practices.

Many applications that make use of sensor networks require secure communication. Because asymmetric-key solutions are difficult to implement in such a resource-constrained environment, symmetric-key methods coupled with a priori key distribution schemes have been proposed to achieve the goals of data secrecy and integrity. These approaches typically assume that all nodes are similar in terms of capabilities and, hence, deploy the same number of keys in all sensors in a network to provide the aforementioned protections. In this paper, we demonstrate that a probabilistic unbalanced distribution of keys throughout the network that leverages the existence of a small percentage of more capable sensor nodes can not only provide an equal level of security, but also reduce the consequences of node compromise. To fully characterize the effects of the unbalanced key management system, we design, implement, and measure the performance of a complementary suite of key establishment protocols known as LIGER. Using their predeployed keys, nodes operating in isolation from external networks can securely and efficiently establish keys with each other. Should resources such as a backhaul link to a key distribution center (KDC) become available, networks implementing LIGER automatically incorporate and benefit from such facilities. Detailed experiments demonstrate that the unbalanced distribution in combination with the multimodal LIGER suite offers a robust and practical solution to the security needs in sensor networks

Attributes define, classify, or annotate the datum to which they are assigned. However, traditional attribute architectures and cryptosystems are ill-equipped to provide security in the face of diverse access requirements and environments. In this paper, we introduce a novel secure information management architecture based on emerging attribute-based encryption (ABE) primitives. A policy system that meets the needs of complex policies is defined and illustrated. Based on the needs of those policies, we propose cryptographic optimizations that vastly improve enforcement efficiency. We further explore the use of such policies in two example applications: a HIPAA compliant distributed file system and a social network. A performance analysis of our ABE system and example applications demonstrates the ability to reduce cryptographic costs by as much as 98% over previously proposed constructions. Through this, we demonstrate that our attribute system is an efficient solution for securely managing information in large, loosely-coupled, distributed systems.

Abstract — Many applications that make use of sensor networks require secure communication. Because asymmetric-key solutions are difficult to implement in such a resource-constrained environment, symmetric-key methods coupled with a priori key distribution schemes have been proposed to achieve the goals of data secrecy and integrity. These approaches typically assume that all sensors are similar in terms of capabilities, and hence deploy the same number of keys in all sensors in a network to provide the aforementioned protections. In this paper we demonstrate that a probabilistic unbalanced distribution of keys throughout the network that leverages the existence of a small percentage of more capable sensor nodes can not only provide an equal level of security but also reduce the consequences of node compromise. We demonstrate the effectiveness of this approach on small networks using a variety of trust models and then demonstrate the application of this method to very large systems. The approach and analysis presented in this paper can be applied to all protocols that use probabilistic keys including those that employ broadcast mechanisms, hash functions or polynomials for the generation of keys.

The transformation of telecommunications networks from homogeneous closed systems providing only voice services to Internet-connected open networks that provide voice and data services presents significant security challenges. For example, recent research illustrated that a carefully crafted DoS attack via text messaging could incapacitate all voice communications in a metropolitan area with little more than a cable modem. This attack highlights a growing threat to these systems; namely, cellular networks are increasingly exposed to adversaries both in and outside the network. In this paper, we use a combination of modeling and simulation to demonstrate the feasibility of targeted text messaging attacks. Under realistic network conditions, we show that adversaries can achieve blocking rates of more than 70% with only limited resources. We then develop and characterize five techniques from within two broad classes of countermeasures - queue management and resource provisioning. Our analysis demonstrates that these techniques can eliminate or extensively mitigate even the most intense targeted text messaging attacks. We conclude by considering the tradeoffs inherent to the application of these techniques in current and next generation telecommunications networks.

The majority of security schemes available for sensor networks assume deployment in areas without access to a wired infrastructure. More specifically, nodes in these networks are unable to leverage key distribution centers (KDCs) to assist them with key management. In networks with a heterogeneous mix of nodes, however, it is not unrealistic to assume that some more powerful nodes have at least intermittent contact with a backbone network. For instance, an air-deployed battlefield network may have to operate securely for some time until uplinked friendly forces move through the area. We therefore propose LIGER, a hybrid key management scheme for heterogeneous sensor networks that allows systems to operate in both the presence and absence of a KDC. Specifically, when no KDC is available, nodes communicate securely with each other based upon a probabilistic unbalanced method of key management. The ability to access a KDC allows nodes to probabilistically authenticate neighboring devices with which they are communicating. We also demonstrate that this scheme is robust to the compromise of both low and high capability nodes and that the same keys can be used for both modes of operation. Detailed experiments and simulations are used to show that LIGER is a highly practical solution for the current generation of sensors and the unbalanced approach can significantly reduce network initialization time.

Mobility management protocols allow wireless devices to move between networks. These protocols have traditionally supported the mobility of individual nodes and are therefore not optimized to support the migration of groups. Accordingly, the time required to re-establish connectivity, frequency of dropped packets and contention for the air interface increase significantly for mobile groups. We propose a protocol for mobile groups that reduces all of the above by allowing a single node to perform handoffs on behalf of all group members. This "gateway" node eliminates the need for multiple handoff messages by obscuring group membership to external parties. Through extensive simulation and implementation, we show significant reduction in handoff times, message complexity and packet loss for groups of heterogeneous, mobile sensors running AODV and DSDV. By leveraging the naturally occurring hierarchy, we demonstrate that it is possible for groups to efficiently use traditional mobility protocols to support their collective movements.

Secure data dissemination in wireless ad hoc and sensor networks has recently received a great deal of attention. A variety of protocols have been proposed in order to ensure secure data delivery across these systems; however, the majority of these schemes assume the presence of public or pre-established symmetric keys. Accordingly, the cost of key management has not been incorporated into secure routing mechanisms in this setting. This paper considers the expenses incurred by sensor networks implementing secure routing schemes on top of probabilistic symmetric key management schemes. Specifically, we examine the overhead observed from proactive and reactive key establishment mechanisms for networks using a balanced method of key management. Through extensive simulation, we quantify more realistic costs for the application of secure hop-by-hop routing in sensor networks

Cellular networks are a critical component of the economic and social infrastructures in which we live. In addition to voice services, these networks deliver alphanumeric text messages to the vast majority of wireless subscribers. To encourage the expansion of this new service, telecommunications companies offer connections between their networks and the Internet. The ramifications of such connections, however, have not been fully recognized. In this paper, we evaluate the security impact of the SMS interface on the availability of the cellular phone network. Specifically, we demonstrate the ability to deny voice service to cities the size of Washington D.C. and Manhattan with little more than a cable modem. Moreover, attacks targeting the entire United States are feasible with resources available to medium-sized zombie networks. This analysis begins with an exploration of the structure of cellular networks. We then characterize network behavior and explore a number of reconnaissance techniques aimed at effectively targeting attacks on these systems. We conclude by discussing countermeasures that mitigate or eliminate the threats introduced by these attacks.