The 5G-AKA protocol, defined by 3GPP for authentication and key agreement in 5G networks, remains vulnerable to linkability, synchronization failure, and Sequence Number (SQN) exposure attacks. These issues threaten user privacy and service availability. Existing improvements often retain these flaws or cause high overhead due to continued use of the legacy SQN mechanism from 3G. In this paper, we propose 5G-RNAKA, a secure and efficient AKA protocol for 5G systems. Unlike 5G-AKA, 5G-RNAKA eliminates SQN counters and instead utilizes random numbers generated by the Universal Subscriber Identity Module (USIM) in 5G User Equipment (UE) for session identification. This random number is embedded in the reply message from the service network (SN) to prevent replay attacks against the UE. Additionally, by removing the SQN mechanism, 5G-RNAKA enhances user privacy by preventing attackers from linking challenge-response sessions. It also enables the UE to authenticate the SN, effectively mitigating the risk of SN impersonation. We formally verify that 5G-RNAKA achieves its security goals of privacy, authentication, and secrecy using the state-of-the-art formal verification tool, Tamarin Prover. Our implementation and evaluation further demonstrate that 5G-RNAKA improves communication efficiency and reduces storage overhead. While primarily designed for 5G, 5G-RNAKA's features align with emerging trends in 6G authentication, suggesting its potential for adaptation to future 6G architectures.

Microcontroller systems are integral to our daily lives, powering mission-critical applications such as vehicles, medical devices, and industrial control systems. Therefore, it is essential to investigate and outline the challenges encountered in developing secure microcontroller systems. While previous research has focused solely on microcontroller firmware analysis to identify and characterize vulnerabilities, our study uniquely leverages data from the 2023 and 2024 MITRE eCTF team submissions and post-competition interviews. This approach allows us to dissect the entire lifecycle of secure microcontroller system development from both technical and perceptual perspectives, providing deeper insights into how these vulnerabilities emerge in the first place.

No abstract available.

Biometric authentication schemes, i.e., fingerprint and face authentication, raise serious privacy concerns. To alleviate such concerns, hand authentication has been proposed recently. Existing hand authentication schemes, however, use dedicated hardware, such as infrared or depth cameras, which are not available on commodity mobile devices. In this paper, we presentEchoHand, a high accuracy and presentation attack resistant authentication scheme that complements camera-based 2-dimensional hand geometry recognition of one hand with an active acoustic sensing of the other hand. To this end,EchoHandplays an inaudible acoustic signal using the speaker to actively sense the holding hand and collects the echoes using the microphone.EchoHanddoes not rely on any specialized hardware but uses the built-in speaker, microphone and camera.EchoHanddoes not place more burdens on users than existing hand authentication methods. We conduct comprehensive experiments to evaluate the reliability, security, and usability ofEchoHand. The results show thatEchoHandhas a low equal error rate of 2.45% with as few as 10 training data points and it defeats presentation attacks. The results of the user study also suggest that the required hand gestures are easy to perform, andEchoHandis very user-friendly with low latency.

Smart contracts, the cornerstone of decentralized applications, have become increasingly prominent in revolutionizing the digital landscape. However, vulnerabilities in smart contracts pose great risks to user assets and undermine overall trust in decentralized systems. Fuzzing, a prominent security testing technique, is extensively explored to detect vulnerabilities. But current smart contract fuzzers fall short of expectations in testing efficiency for two primary reasons. Firstly, smart contracts are stateful programs, and existing approaches, primarily coverage-guided, lack effective feedback from the contract state. Consequently, they struggle to effectively explore the contract state space. Secondly, coverage-guided fuzzers, aiming for comprehensive program coverage, may lead to a wastage of testing resources on benign code areas. This wastage worsens in smart contract testing, as the mix of code and state spaces further complicates comprehensive testing. To address these challenges, we propose Vulseye, a stateful directed graybox fuzzer for smart contracts guided by vulnerabilities. Different from prior works, Vulseyeachieves stateful directed fuzzing by prioritizing testing resources to code areas and contract states that are more prone to vulnerabilities. We introduceCode TargetsandState Targetsinto fuzzing loops as the testing targets of Vulseye. We use static analysis and pattern matching to pinpointCode Targets, and propose a scalable backward analysis algorithm to specifyState Targets. We design a novel fitness metric that leverages feedback from both the contract code space and state space, directing fuzzing toward these targets. With the guidance of code and state targets, Vulseyealleviates the wastage of testing resources on benign code areas and achieves effective stateful fuzzing. In comparison with state-of-the-art fuzzers, Vulseyedemonstrated superior effectiveness and efficiency. Notably, it uncovered 4,845 vulnerabilities in 42,738 real-world smart contracts, outperforming existing approaches by up to$9.7\times $, and identified 11 previously unknown vulnerabilities within the top 50 Ethereum DApps, involving approximately 2,500,000 USD.

The advent of decentralized finance has ushered in a transformative era in the financial sector, leveraging blockchain technology to facilitate peer-to-peer transactions without traditional intermediaries. Amidst this innovation, the DeFi landscape faces the pervasive threat of rugpulls, where developers abruptly abandon projects post-fundraising, leaving investors with devalued assets. This growing concern highlights a critical research gap in the proactive detection and prevention of such fraudulent schemes. To combat this, we propose RUGSCREENER, a temporal graph neural network-based solution to identify rugpull risks within DeFi transactions. It employs a dynamic representation of blockchain interactions, enriched with comprehensive node attributes and effective temporal graph learning techniques based on memory and attention mechanisms, effectively capturing the rapid-moving and complex transaction patterns indicative of potential fraud. Our evaluation is based on a newly compiled Ethereum dataset that includes two subsets: an unlabeled set with 1,882,114 transactions from 29,595 tokens for temporal graph representation learning, and a labeled set with 128,819 transactions from 1,000 tokens (500 rugpull and 500 benign) for downstream evaluation. Using this dataset, RUGSCREENER achieves a balanced accuracy of 95.7% in detecting rugpull tokens. Our extensive evaluation, utilizing the Ethereum dataset comprising 1000 tokens, showcases its robust performance with a balanced accuracy of 95.7% in detecting rugpull tokens. Remarkably, RUGSCREENER surpasses existing state-of-the-art graph learning models in detecting rugpull tokens with enhanced accuracy and reliability.

Quantum Federated Learning (QFL) has emerged as a promising research direction by combining the strengths of quantum computing and federated learning. However, existing QFL solutions have consistently failed to simultaneously improve client training efficiency and ensure communication security. In this paper, we present a novel Multi-qubit Broadcast-based QFL framework (MB-QFL) to address the efficiency and security challenges of existing approaches. The framework employs a novel multi-qubit broadcast protocol and a quantum average method to secure the information transmission process. The multi-qubit broadcast protocol overcomes the limitations of existing protocols by allowing the transmission of an arbitraryS-qubit state from one sender to multiple (Q) receivers, whereas earlier protocols were restricted to broadcast one or two qubit state to recipients. Additionally, we propose an averaging method for quantum states, which exploits the probabilistic cloning technique to achieve aggregation in MB-QFL. The security analysis demonstrates that MB-QFL can effectively protect against inference attacks from malicious clients, as well as eavesdropping and intercept-and-resend attacks during communication. The algorithm complexity of MB-QFL is significantly lower than existing QFLs. Besides, the experimental results indicate that MB-QFL achieves higher classification accuracy than other QFLs.

Despite of their benefits, existing Trusted Execution Environments (TEE) or enclaves have been criticized for lack of transparency, vulnerabilities, and various restrictions. A significant limitation is that they only provide a static and fixed hardware Trusted Computing Base (TCB) that cannot be customized for different applications. The design violates the principle of least privilege by including unnecessary peripherals in the hardware TCB and buggy peripheral drivers in the software TCB. Additionally, Existing TEEs time-share a processor core with the Rich Execution Environment (REE), making execution less efficient and vulnerable to cache side-channel attacks. Although many previous projects have focused on addressing software issues in TEEs on SGX, TrustZone, or RISC-V, some TEE issues are inherent in the hardware system's design, making them impossible to resolve with software alone.

Voice Personal Assistants (VPA) are becoming popular entry points to control connected devices in an IoT environment, e.g., by invoking Amazon Alexa voice-apps (called skills) to turn on/off lights through voice commands. Amazon Alexa platform allows third-party developers to build skills and publish them to marketplaces, which greatly extends the functionalities of VPA. Despite the many convenient features, there are increasing security and safety concerns about VPA-controlled IoT systems. Previous research demonstrated the prevalence of potentially malicious or problematic skills in the marketplace. However, existing works mainly focus on non-IoT skills (e.g., skills under the Kids and Health categories). The security and safety risks of IoT skills are largely under-explored.

Decentralized finance has experienced phenomenal growth, revolutionizing the landscape of financial transactions and asset management via blockchain. Yet, this swift growth brings with it substantial challenges, notably the surge in scam tokens, imposing significant security threats on cryptocurrency investments and trading. Existing detection methods of scam token, primarily relying on analyzing contract codes or transaction patterns, struggle to catch increasingly sophisticated tactics employed by scammers. For example, contract-based analysis are unable to identify scams lacking overt malicious code, e.g., most rugpulls, while transaction-based methods generally lack the foresight to early-detect potential risks.

Real-time and embedded systems are predominantly written in C, a language that is notoriously not memory safe. This has led to widespread memory-corruption vulnerabilities in real-time embedded cyber-physical systems (CPS). This is concerning, as such devices are becoming increasingly networked with the Internet of Things (IoT) and other communication technologies (e.g., 5G), rendering them vulnerable to remote attacks. Attackers have demonstrated how memory-corruption vulnerabilities can be used to hijack program control flow to implement arbitrary attacker-controlled logic. One class of defenses that has been developed to prevent such attacks is called control-flow integrity (CFI), which applies checks at control-flow transitions to ensure the target is valid. Unfortunately, attackers have shown how to divert control flow to seemingly valid targets in an invalid and malicious sequence. This paper presents InsectACIDE, the first holistic CFI for embedded and real-time systems that does not require binary instrumentation and that is context sensitive, i.e., it checks that the sequence of control-flow transitions taken is valid, not just individual transitions, thereby detecting such attacks. InsectACIDE is implemented on an embedded Cortex-M processor using the TrustZone trusted execution environment, and holistic context-sensitive CFI is enforced for both applications and the kernel. InsectACIDE uses hardware debugging features on the Cortex-M processor and therefore does not require any kernel or application binary modification. Experimental results show that InsectACIDE incurs significantly less runtime overhead compared to the state-of-the-art holistic CFI solution. Real-time schedulability analysis is presented, along with a schedulability evaluation, to demonstrate the tradeoff between stronger protection and real-time schedulability.

Online hate is an escalating problem that negatively impacts the lives of Internet users, and is also subject to rapid changes due to evolving events, resulting in new waves of online hate that pose a critical threat. Detecting and mitigating these new waves present two key challenges: it demands reasoning-based complex decision-making to determine the presence of hateful content, and the limited availability of training samples hinders updating the detection model. To address this critical issue, we present a novel framework called HateGuard for effectively moderating new waves of online hate. HateGuard employs a reasoning-based approach that leverages the recently introduced chain-of-thought (CoT) prompting technique, harnessing the capabilities of large language models (LLMs). HateGuard further achieves prompt-based zero-shot detection by automatically generating and updating detection prompts with new derogatory terms and targets in new wave samples to effectively address new waves of online hate. To demonstrate the effectiveness of our approach, we compile a new dataset consisting of tweets related to three recently witnessed new waves: the 2022 Russian invasion of Ukraine, the 2021 insurrection of the US Capitol, and the COVID-19 pandemic. Our studies reveal crucial longitudinal patterns in these new waves concerning the evolution of events and the pressing need for techniques to rapidly update existing moderation tools to counteract them. Comparative evaluations against state-of-the-art approaches illustrate the superiority of our framework, showcasing a substantial 10.59% to 88% improvement in detecting the three new waves of online hate. Our work highlights the severe threat posed by the emergence of new waves of online hate and represents a paradigm shift in addressing this threat practically.

Transfer learning, successful in knowledge translation across related tasks, faces a substantial privacy threat from membership inference attacks (MIAs). These attacks, despite posing significant risk to ML model’s training data, remain limited-explored in transfer learning. The interaction between teacher and student models in transfer learning has not been thoroughly explored in MIAs, potentially resulting in an under-examined aspect of privacy vulnerabilities within transfer learning. In this paper, we propose a new MIA vector against transfer learning, to determine whether a specific data point was used to train the teacher model while only accessing the student model in a white-box setting. Our method delves into the intricate relationship between teacher and student models, analyzing the discrepancies in hidden layer representations between the student model and its shadow counterpart. These identified differences are then adeptly utilized to refine the shadow model’s training process and to inform membership inference decisions effectively. Our method, evaluated across four datasets in diverse transfer learning tasks, reveals that even when an attacker only has access to the student model, the teacher model’s training data remains susceptible to MIAs. We believe our work unveils the unexplored risk of membership inference in transfer learning.

WiFi devices are ubiquitous and may leak user and household privacy. In this paper, we report an attack, namely WiFiLeaks, which uses a commodity mobile device to passively detect stationary human presence through a wall by analyzing the channel state information of wireless signals transmitted by indoor WiFi devices. In our adversarial scenario, attackers cannot control the WiFi transmitter or use advanced radio devices. The main challenge of this attack is how to extract robust features from non-customized signals for stationary human presence. To address this challenge, we first combine methods based on outliers and wavelet denoising to enhance the low-frequency information related to human presence. Then we propose a novel feature extraction method based on the correlation among subcarriers since stationary human presence can enhance their correlations. We evaluate WiFiLeaks using nine different WiFi transmitter and one commodity smartphone in four different settings. The evaluations show WiFiLeaks can still achieve accuracy rates of 83.33% and 100% for human presence and absence at 20 meters between the monitor device and the transmitter in through-the-wall scenarios.

Recent studies regarding the COVID-19 pandemic have revealed the widespread propagation of hateful content during this period. While significant research has focused on COVID-19-related online hate in text (e.g., text-based tweets), the role of memes in propagating online hate during the pandemic has been largely overlooked. Memes are a popular mechanism used by Internet users to convey their thoughts and opinions on a variety of topics. However, memes have emerged as an important mechanism through which ideologically potent and hateful content spreads on social media platforms. In this work, we focus on investigating the role of memes in the propagation of online hate during the COVID-19 pandemic. We first collect a novel dataset of 4,001 COVID-19-related hateful memes and their replies over a 3-year period from Twitter. Then, we carry out the first large-scale investigation into the impact of these memes on Twitter users, by studying the psychological reactions of Twitter users to these memes using various text analysis methods. We find that COVID-19-related hateful memes have a significantly greater negative impact on Twitter users in comparison to text-based hateful tweets, and increasing negativity towards such memes over the 3-year period. Our new dataset of COVID-19-related hateful memes and findings from our work pave the way for studying the dissemination and moderation of COVID-19-related online hate through the medium of memes.

Microcontroller-based embedded systems are often programmed in low-level languages and are vulnerable to control-flow hijacking attacks. One approach to prevent such attacks is to enforce control-flow integrity (CFI), but inlined CFI enforcement can pose challenges in embedded systems. For example, it increases binary size and changes memory layout. Trace-based control-flow violation detection (CFVD) offers an alternative that doesn't require instrumentation of the protected software or changes to its memory layout. However, existing CFVD methods used in desktop systems require kernel modifications to store and analyze the trace, which limits their use to monitoring unprivileged applications. But, embedded systems are interrupt-driven, with the majority of processing taking place in the privileged mode. Therefore, it is critical to provide a holistic and system-oriented CFVD solution that can monitor control-flow transfers both within and among privileged and unprivileged components.

ARM Cortex-M is one of the most popular microcontroller architectures designed for embedded and Internet of Things (IoT) applications. To facilitate efficient execution, it has some unique hardware optimization. In particular, Cortex-M TrustZone has a fast state switch mechanism that allows direct control-flow transfer from the secure state program to the non-secure state userspace program. In this paper, we demonstrate how this fast state switch mechanism can be exploited for arbitrary code execution with escalated privilege in the non-secure state by introducing a new exploitation technique, namely return-to-non-secure (ret2ns). We experimentally confirmed the feasibility of four variants of ret2ns attacks on two Cortex-M hardware systems. To defend against ret2ns attacks, we design two address sanitizing mechanisms that have negligible performance overhead.

Hate speech has emerged as a major problem plaguing our social spaces today. While there have been significant efforts to address this problem, existing methods are still significantly limited in effectively detecting hate speech online. A major limitation of existing methods is that hate speech detection is a highly contextual problem, and these methods cannot fully capture the context of hate speech to make accurate predictions. Recently, large language models (LLMs) have demonstrated state-of-the-art performance in several natural language tasks. LLMs have undergone extensive training using vast amounts of natural language data, enabling them to grasp intricate contextual details. Hence, they could be used as knowledge bases for context-aware hate speech detection. However, a fundamental problem with using LLMs to detect hate speech is that there are no studies on effectively prompting LLMs for context-aware hate speech detection. In this study, we conduct a large-scale study of hate speech detection, employing five established hate speech datasets. We discover that LLMs not only match but often surpass the performance of current benchmark machine learning models in identifying hate speech. By proposing four diverse prompting strategies that optimize the use of LLMs in detecting hate speech. Our study reveals that a meticulously crafted reasoning prompt can effectively capture the context of hate speech by fully utilizing the knowledge base in LLMs, significantly outperforming existing techniques. Furthermore, although LLMs can provide a rich knowledge base for the contextual detection of hate speech, suitable prompting strategies play a crucial role in effectively leveraging this knowledge base for efficient detection.

The FIDO protocol suite aims at allowing users to log in to remote services with a local and trusted authenticator. With FIDO, relying services do not need to store user-chosen secrets or their hashes, which eliminates a major attack surface for e-business. Given its increasing popularity, it is imperative to formally analyze whether the security promises of FIDO hold. In this paper, we present a comprehensive and formal verification of the FIDO UAF protocol by formalizing its security assumptions and goals and modeling the protocol under different scenarios in ProVerif. Our analysis identifies the minimal security assumptions required for each of the security goals of FIDO UAF to hold. We confirm previously manually discovered vulnerabilities in an automated way and disclose several new attacks. Guided by the formal verification results, we also discovered two practical attacks on two popular Android FIDO apps, which we responsibly disclosed to the vendors. In addition, we offer several concrete recommendations to fix the identified problems and weaknesses in the protocol.

The rocketed population, poor security, and 24/7 online properties make Linux-based Internet of Things (IoT) devices ideal targets for attackers. However, due to the budget constraints and an enormous number of vulnerabilities on such devices, protecting them against attacks is very challenging. Therefore, understanding and detecting IoT malware remote infection, which is before the compromised IoT devices are monetized by adversaries, is crucial to mitigate damages and financial loss caused by IoT malware. In this paper, we conduct an empirical study on a large-scale dataset covering 403,464 samples collected from VirusShare and a large group of IoT honeypots to gain a deep insight into the characteristics of IoT malware remote infection. We share detailed statistics of shell commands found in our dataset, highlight malicious behaviors performed through those commands, investigate current states of fingerprinting methods of those commands, and offer a taxonomy of shell commands by introducing the notion of infection capability. To demonstrate the usefulness of the knowledge gained from our study, we develop an approach to detect ongoing remote infection activities based on infection capabilities. Our evaluation shows that our detection approach can achieve a 99.22% detection rate for remote infections in the wild and introduce small performance overhead.

Biometric authentication schemes, i.e., fingerprint and face authentication, raise serious privacy concerns. To alleviate such concerns, hand authentication has been proposed recently. However, existing hand authentication schemes use dedicated hardware, such as infrared or depth cameras, which are not available on commodity mobile devices. In this paper, we present EchoHand, a high accuracy and presentation attack resistant authentication scheme that complements camera-based 2-dimensional hand geometry recognition of one hand with active acoustic sensing of the other holding hand. EchoHand plays an inaudible acoustic signal using the speaker to actively sense the holding hand and collects the echoes using the microphone. EchoHand does not rely on any specialized hardware but uses the built-in speaker, microphone and camera. Moreover, EchoHand does not place more burdens on users than existing hand authentication methods. We conduct comprehensive experiments to evaluate the reliability and security of EchoHand. The results show that EchoHand has a low equal error rate of 2.45% with as few as 10 training data points and it defeats presentation attacks.

A large number of photos shared online often contain private user information, which can cause serious privacy breaches when viewed by unauthorized users. Thus, there is a need for more efficient privacy control that requires automatic detection of users' private photos. However, the automatic detection of users' private photos is a challenging task, since different users may have different privacy concerns and a generalized one-size-fits-all approach for private photo detection would not be suitable for most users. User-specific detection of private photos should, therefore, be investigated. Furthermore, for effective privacy control, the exact sensitive regions in private photos need to be pinpointed, so that sensitive content can be protected via different privacy control methods. In this paper, we propose a novel system, AutoPri, to enable automatic and user-specific content-based photo privacy control in online social networks. We collect a large dataset of 31, 566 private and public photos from real-world users and present important observations on photo privacy concerns. Our system can automatically detect private photos in a user-specific manner using a detection model based on a multimodal variational autoencoder and pinpoint sensitive regions in private photos with an explainable deep learning-based approach. Our evaluations show that AutoPri can effectively determine user-specific private photos with high accuracy (94.32%) and pinpoint exact sensitive regions in them to enable effective privacy control in user-centered online social networks.

Fingerprint authentication has gained increasing popularity on mobile devices in recent years. However, it is vulnerable to presentation attacks, which include that an attacker spoofs with an artificial replica. Many liveness detection solutions have been proposed to defeat such presentation attacks; however, they all fail to defend against a particular type of presentation attack, namelypuppet attack, in which an attacker places an unwilling victim's finger on the fingerprint sensor. In this article, we proposeFinAuth, an effective and efficient software-only solution, to complement fingerprint authentication by defeating both synthetic spoofs and puppet attacks usingfingertip-touchcharacteristics.FinAuthcharacterizes intrinsic fingertip-touch behaviors including the acceleration and the rotation angle of mobile devices when a legitimate user authenticates.FinAuthonly utilizes common sensors equipped on mobile devices and does not introduce extra usability burdens on users. To evaluate the effectiveness ofFinAuth, we carried out experiments on datasets collected from 90 subjects after the IRB approval. The results show thatFinAuthcan achieve the average balanced accuracy of 96.04% with 5 training data points and 99.28% with 100 training data points. Security experiments also demonstrate thatFinAuthis resilient against possible attacks. In addition, we report the usability analysis results ofFinAuth, including user authentication delay and overhead.

Most of the existing mobile application (app) vetting mechanisms only estimate risks at a coarse-grained level by analyzing app syntax but not semantics. We propose a semantics-aware privacy risk assessment framework (SPRisk), which considers the sensitivity discrepancy of privacy-related factors at semantic level. Our framework can provide qualitative (i.e., risk level) and quantitative (i.e., risk score) assessment results, both of which help users make decisions to install an app or not. Furthermore, to find the reasonable weight distribution of each factor automatically, we exploit a self-learning weight assignment method, which is based on fuzzy clustering and knowledge dependency theory. We implement a prototype system and evaluate the effectiveness of SPRisk with 192,445 normal apps and 7,111 malicious apps. A measurement study further reveals some interesting findings, such as the privacy risk distribution of Google Play Store, the diversity of official and unofficial marketplaces, which provide insights into understanding the seriousness of privacy threat in the Android ecosystem.

We present DANdroid, a novel Android malware detection model using a deep learning Discriminative Adversarial Network (DAN) that classifies both obfuscated and unobfuscated apps as either malicious or benign. Our method, which we empirically demonstrate is robust against a selection of four prevalent and real-world obfuscation techniques, makes three contributions. Firstly, an innovative application of discriminative adversarial learning results in malware feature representations with a strong degree of resilience to the four obfuscation techniques. Secondly, the use of three feature sets; raw opcodes, permissions and API calls, that are combined in a multi-view deep learning architecture to increase this obfuscation resilience. Thirdly, we demonstrate the potential of our model to generalize over rare and future obfuscation methods not seen in training. With an overall dataset of 68,880 obfuscated and unobfuscated malicious and benign samples, our multi-view DAN model achieves an average F-score of 0.973 that compares favourably with the state-of-the-art, despite being exposed to the selected obfuscation methods applied both individually and in combination.

Cache side-channel attacks abuse microarchitectural designs meant to optimize memory access to infer information about victim processes, threatening data privacy and security. Recently, the ARM architecture has come into the spotlight of cache side-channel attacks with its unprecedented growth in the market.

Organizations, such as companies and governments, created Security Operations Centers (SOCs) to defend against computer security attacks. SOCs are central defense groups that focus on security incident management with capabilities such as monitoring, preventing, responding, and reporting. They are one of the most critical defense components of a modern organization's defense. Despite their critical importance to organizations, and the high frequency of reported security incidents, only a few research studies focus on problems specific to SOCs. In this study, to understand and identify the issues of SOCs, we conducted 18 semi-structured interviews with SOC analysts and managers who work for organizations from different industry sectors. Through our analysis of the interview data, we identified technical and non-technical issues that exist in SOC. Moreover, we found inherent disagreements between SOC managers and their analysts that, if not addressed, could entail a risk to SOC efficiency and effectiveness. We distill these issues into takeaways that apply both to future academic research and to SOC management. We believe that research should focus on improving the efficiency and effectiveness of SOCs.

The studies on underground forums and marketplaces have significantly advanced our understandings of cybercrime workflows and underground economies. Researchers of underground economies have conducted comprehensive studies on public interactions. However, little research focuses on private interactions. The lack of the investigation on private interactions may cause misunderstandings on underground economies, as users in underground forums and marketplaces tend to share the minimal amount of information in public interactions and resort to private messages for follow-up conversations. In this paper, we propose methods to investigate the underground private interactions and we analyze a recently leaked dataset from Nulled.io. We present analyses on the contents and purposes of private messages. In addition, we design machine learning-based models that only use the publicly available information to detect if two underground users privately communicate with each other. Finally, we perform adversarial analysis to evaluate the robustness of the detector to different types of attacks.

Recently, applications that deliver customized content to end-users, e.g., digital objects on top of a video stream, depending on information such as their current physical location, usage patterns, personal data, etc., have become extremely popular. Despite their promising future, some concerns still exist with respect to the proper use of such space-sensitive applications (S-Apps) inside independently-run physical spaces, e.g., schools, museums, hospitals, memorials, etc. Based on the idea that innovative technologies should be paired with novel (and effective) security measures, this paper proposes space-sensitive access control (SSAC), an approach for restricting space-sensitive functionality in such independently-run physical spaces, allowing for the specification, evaluation and enforcement of rich and flexible authorization policies, which, besides meeting the specific needs for S-Apps, are also intended to avoid the need for interruptions in their normal use as well as repetitive policy updates, thus providing a convenient solution for both policy makers and end-users. We present a theoretical model, a proof-of-concept S-App, and a supporting API framework, which facilitate the policy crafting, storage, retrieval and evaluation processes, as well as the enforcement of authorization decisions. In addition, we present a performance case study depicting our proof-of-concept S-App in a set of realistic scenarios, as well as a user study which resulted in 90% of participants being able to understand and write authorization policies using our approach, and 93% of them also recognizing the need for restricting functionality in the context of emerging space-sensitive technologies, thus providing evidence that encourages the adoption of SSAC in practice.

Wi-Fi Calling, which is used to make and receive calls over the Wi-Fi network, has been widely adopted and deployed to extend the coverage and increase the capacity in weak signal areas by moving traffic from LTE to Wi-Fi networks. However, the security of Wi-Fi Calling mechanism has not been fully analyzed, and Wi-Fi Calling may inherently have greater security risks than conventional LTE calling. To provide secure connections with confidentiality and integrity, Wi-Fi Calling leverages the IETF protocols IKEv2 and IPSec.

The security of ARM TrustZone relies on the idea of splitting system-on-chip hardware and software into two worlds, namely normal world and secure world. In this paper, we report cross-world covert channels, which exploit the world-shared cache in the TrustZone architecture. We design a Prime+Count technique that only cares about how many cache sets or lines have been occupied. The coarser-grained approach significantly reduces the noise introduced by the pseudo-random replacement policy and world switching. Using our Prime+Count technique, we build covert channels in single-core and cross-core scenarios in the TrustZone architecture. Our results demonstrate that Prime+Count is an effective technique for enabling cross-world covert channels on ARM TrustZone.

Network Management is a critical process for an enterprise to configure and monitor the network devices using cost effective methods. It is imperative for it to be robust and free from adversarial or accidental security flaws. With the advent of cloud computing and increasing demands for centralized network control, conventional management protocols like SNMP appear inadequate and newer techniques like NMDA and NETCONF have been invented. However, unlike SNMP which underwent improvements concentrating on security, the new data management and storage techniques have not been scrutinized for the inherent security flaws. In this paper, we identify several vulnerabilities in the widely used critical infrastructures which leverage the Network Management Datastore Architecture design (NMDA). Software Defined Networking (SDN), a proponent of NMDA, heavily relies on its datastores to program and manage the network. We base our research on the security challenges put forth by the existing datastore's design as implemented by the SDN controllers. The vulnerabilities identified in this work have a direct impact on the controllers like OpenDayLight, Open Network Operating System and their proprietary implementations (by CISCO, Ericsson, RedHat, Brocade, Juniper, etc). Using our threat detection methodology, we demonstrate how the NMDA-based implementations are vulnerable to attacks which compromise availability, integrity, and confidentiality of the network. We finally propose defense measures to address the security threats in the existing design and discuss the challenges faced while employing these countermeasures.

We present SeCore, which is a novel continuous extrospection system on multi-core ARM platform. SeCore leverages ARM TrustZone technology to keep one core in the secure world and assure the integrity of the static kernel data and code in the normal world. By breaking the original time-sharing paradigm of such systems, SeCore enables continuous coprocessor-like monitoring with high visibility into the rich execution environment on mobile and IoT platforms. By ensuring that secure tools execute on certain physical CPU cores, the system's attack surface is also significantly reduced.

FIDO protocols enable online services to leverage native authenticators of end-user computing devices including fingerprint readers for authentication to replace or complement passwords. FIDO protocols also offer support for prompting a user to confirm a specific transaction. However, due to the lack of a trusted display module in most Authenticators, operating systems of user devices display transaction contents directly on the main screen. In the paper, we demonstrate an attack on FIDO transaction confirmation in which malicious applications leverage the disparity between the displayed and actual transaction contents to trick users into confirming falsified transactions. In addition, we propose a lightweight secure display mechanism for FIDO transaction confirmations on mobile devices by leveraging the ARM TrustZone technology.

In recent years, we witnessed a drastic increase of ransomware, especially on popular mobile platforms including Android. Ransomware extorts victims for a sum of money by taking control of their devices or files. In light of their rapid growth, there is a pressing need to develop effective countermeasure solutions. However, the research community is still constrained by the lack of a comprehensive data set, and there exists no insightful understanding of mobile ransomware in the wild. In this paper, we focus on the Android platform and aim to characterize existing Android ransomware. Specifically, we have managed to collect 2,721 ransomware samples that cover the majority of existing Android ransomware families. Based on these samples, we systematically characterize them from several aspects, including timeline and malicious features. In addition, the detection results of existing anti-virus tools are rather disappointing, which clearly calls for customized anti-mobile-ransomware solutions. To detect ransomware that extorts users by encrypting data, we propose a novel real-time detection system, called RansomProber. By analyzing the user interface widgets of related activities and the coordinates of users' finger movements, RansomProber can infer whether the file encryption operations are initiated by users. The experimental results show that RansomProber can effectively detect encrypting ransomware with high accuracy and acceptable runtime performance.

In this paper, we propose a novel android malware detection system that uses a deep convolutional neural network (CNN). Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. Features indicative of malware are automatically learned by the network from the raw opcode sequence thus removing the need for hand-engineered malware features. The training pipeline of our proposed system is much simpler than existing n-gram based malware detection methods, as the network is trained end-to-end to jointly learn appropriate features and to perform classification, thus removing the need to explicitly enumerate millions of n-grams during training. The network design also allows the use of long n-gram like features, not computationally feasible with existing methods. Once trained, the network can be efficiently executed on a GPU, allowing a very large number of files to be scanned quickly.

In the Android system design, any app can start another app's public components to facilitate code reuse by sending an asynchronous message called Intent. In addition, Android also allows an app to have private components that should only be visible to the app itself. However, malicious apps can bypass this system protection and directly invoke private components in vulnerable apps through a class of newly discovered vulnerability, which is called next-intent vulnerability. In this paper, we design an intent flow analysis strategy which accurately tracks the intent in smali code to statically detect next-intent vulnerabilities efficiently and effectively on a large scale. We further propose an automated approach to dynamically verify the discovered vulnerabilities by generating exploit apps. Then we implement a tool named NIVAnalyzer and evaluate it on 20,000 apps downloaded from Google Play. As the result, we successfully confirms 190 vulnerable apps, some of which even have millions of downloads. We also confirmed that an open-source project and a third-party SDK, which are still used by other apps, have next intent vulnerabilities.

Firewalls have been typically used to enforce network access control. Network Functions Virtualization (NFV) envisions to implement firewall function as software instance (a.k.a virtual firewall). Virtual firewall provides great flexibility and elasticity, which are necessary to protect virtualized environments. In this poster, we propose an innovative virtual firewall controller, VFW Controller, which enables safe, efficient and cost-effective virtual firewall elasticity control. In addition, we implement the core components of VFW Controller on top of NFV and SDN environments. Our experimental results demonstrate that VFW Controller is efficient to provide safe elasticity control of virtual firewalls.

Many service providers including large enterprises have released their own applications (apps) that incorporate HTTP clients to facilitate the communications with their servers. The workflows of and APIs used by a web app and its corresponding mobile app are not always the same. We call the APIs found in apps private web APIs in that they are only supposed to be invoked by apps that developed by the service providers themselves. However, checking the origin of an HTTP request is very difficult, and private web APIs can be easily invoked by other entities. Hence, it is imperative to study if private web APIs provide the same level of security checks and validations as their public counterparts. To automatically discover the undocumented private APIs in Android apps, we design a system that uses static analysis to find the activities that invoke web APIs. Our system then runs the discovered activities on a customized Android system to monitor its HTTP requests and responses. We evaluated our system on 76 popular apps on the Google Play market. Our system successfully run 48 apps and discovered many private server-side APIs from more than 30 apps. Further manual investigation discovered that 9 of the apps have vulnerabilities that would enable API misuse and session hijacking.

OpenFlow, as the prevailing technique for Software-Defined Networks (SDNs), introduces significant programmability, granularity, and flexibility for many network applications to effectively manage and process network flows. However, because OpenFlow attempts to keep the SDN data plane simple and efficient, it focuses solely on L2/L3 network transport and consequently lacks the fundamental ability of stateful forwarding for the data plane. Also, OpenFlow provides a very limited access to connection-level information in the SDN controller. In particular, for any network access management applications on SDNs that require comprehensive network state information, these inherent limitations of OpenFlow pose significant challenges in supporting network services. To address these challenges, we propose an innovative connection tracking framework called STATEMON that introduces a global state-awareness to provide better access control in SDNs. STATEMON is based on a lightweight extension of OpenFlow for programming the stateful SDN data plane, while keeping the underlying network devices as simple as possible. To demonstrate the practicality and feasibility of STATEMON, we implement and evaluate a stateful network firewall and port knocking applications for SDNs, using the APIs provided by STATEMON. Our evaluations show that STATEMON introduces minimal message exchanges for monitoring active connections in SDNs with manageable overhead (3.27% throughput degradation).

Telephone spam costs United States consumers $8.6 billion annually. In 2014, the Federal Trade Commission has received over 22 million complaints of illegal and wanted calls. Telephone spammers today are leveraging recent technical advances in the telephony ecosystem to distribute massive automated spam calls known as robocalls. Given that anti-spam techniques and approaches are effective in the email domain, the question we address is: what are the effective defenses against spam calls? In this paper, we first describe the telephone spam ecosystem, specifically focusing on the differences between email and telephone spam. Then, we survey the existing telephone spam solutions and, by analyzing the failings of the current techniques, derive evaluation criteria that are critical to an acceptable solution. We believe that this work will help guide the development of effective telephone spam defenses, as well as provide a framework to evaluate future defenses.

As smartphones have become an indispensable part of daily life, mobile users are increasingly relying on them to process personal information with feature-rich applications. This situation requires robust security mechanisms for protecting sensitive applications and data on mobile devices. Android, as one the most popular smartphone operating systems, provides two core security mechanisms, application sandboxing and a permission system. However, recent studies show that these mechanisms are vulnerable to be passed by a variety of attacks. In this paper, we argue for the need of designing and implementing more comprehensive security mechanisms for Android. We realize that mediating Inter-Process Communication (IPC) channels used by Android applications can mitigate prominent attacks effectively and efficiently. Based on this observation, we propose a practical multi-layer security framework called TripleMon to support policy-based mediation on Android IPC. We also discuss and evaluate a proof-of-concept prototype of TripleMon along with the experimental results derived from real malware samples and synthetic attacks.

With the advent of various collaborative sharing mechanisms such as Grids, P2P and Clouds, organizations including private and public sectors have recognized the benefits of being involved in inter-organizational, multi-disciplinary, and collaborative projects that may require diverse resources to be shared among participants. In particular, an environment that often makes use of a group of high-performance network facilities would involve large-scale collaborative projects and tremendously seek a robust and flexible access control for allowing collaborators to leverage and consume resources, e.g., computing power and bandwidth. In this paper, we propose a federated access management scheme that leverages the notion of attributes. Our approach allows resource-sharing organizations to provide distributed provisioning (publication, location, communication, and evaluation) of both attributes and policies for federated access management purposes. Also, we provide a proof-of-concept implementation that leverages distributed hash tables (DHT) to traverse chains of attributes and effectively handle the federated access management requirements devised for inter-organizational resource sharing and collaborations.

Mobile operating systems, such as Apple's iOS and Google's Android, have supported a ballooning market of feature-rich mobile applications. However, helping users understand and mitigate security risks of mobile applications is still an ongoing challenge. While recent work has developed various techniques to reveal suspicious behaviors of mobile applications, there exists little work to answer the following question: are those behaviors necessarily inappropriate? In this paper, we seek an approach to cope with such a challenge and present a continuous and automated risk assessment framework called RiskMon that uses machine-learned ranking to assess risks incurred by users' mobile applications, especially Android applications. RiskMon combines users' coarse expectations and runtime behaviors of trusted applications to generate a risk assessment baseline that captures appropriate behaviors of applications. With the baseline, RiskMon assigns a risk score on every access attempt on sensitive information and ranks applications by their cumulative risk scores. Furthermore, we demonstrate how RiskMon supports risk mitigation with automated permission revocation. We also discuss a proof-of-concept implementation of RiskMon as an extension of the Android mobile platform and provide both system evaluation and usability study of our methodology.

Picture gesture authentication has been recently introduced as an alternative login experience to text-based password on touch-screen devices. In particular, the newly on market Microsoft Windows 8™ operating system adopts such an alternative authentication to complement its traditional text-based authentication. We present an empirical analysis of picture gesture authentication on more than 10,000 picture passwords collected from more than 800 subjects through online user studies. Based on the findings of our user studies, we propose a novel attack framework that is capable of cracking passwords on previously unseen pictures in a picture gesture authentication system. Our approach is based on the concept of selection function that models users’ thought processes in selecting picture passwords. Our evaluation results show the proposed approach could crack a considerable portion of picture passwords under different settings. Based on the empirical analysis and attack results, we comparatively evaluate picture gesture authentication using a set of criteria for a better understanding of its advantages and limitations.

Emulator-based dynamic analysis has been widely deployed in Android application stores. While it has been proven effective in vetting applications on a large scale, it can be detected and evaded by recent Android malware strains that carry detection heuristics. Using such heuristics, an application can check the presence or contents of certain artifacts and infer the presence of emulators. However, there exists little work that systematically discovers those heuristics that would be eventually helpful to prevent malicious applications from bypassing emulator-based analysis. To cope with this challenge, we propose a framework called Morpheus that automatically generates such heuristics. Morpheus leverages our insight that an effective detection heuristic must exploit discrepancies observable by an application. To this end, Morpheus analyzes the application sandbox and retrieves observable artifacts from both Android emulators and real devices. Afterwards, Morpheus further analyzes the retrieved artifacts to extract and rank detection heuristics. The evaluation of our proof-of-concept implementation of Morpheus reveals more than 10,000 novel detection heuristics that can be utilized to detect existing emulator-based malware analysis tools. We also discuss the discrepancies in Android emulators and potential countermeasures.

Mobile operating systems, such as Apple's iOS and Google's Android, have supported a ballooning market of feature-rich mobile applications. However, helping users understand security risks of mobile applications is still an ongoing challenge. While recent work has developed various techniques to reveal suspicious behaviors of mobile applications, there exists little work to answer the following question: are those behaviors necessarily inappropriate? In this paper, we seek an approach to cope with such a challenge and present a continuous and automated risk assessment framework called RiskMon that uses machine-learned ranking to assess risks incurred by users' mobile applications, especially Android applications. RiskMon combines users' coarse expectations and runtime behaviors of trusted applications to generate a risk assessment baseline that captures appropriate behaviors of applications. With the baseline, RiskMon assigns a risk score on every access attempt on sensitive information and ranks applications by their cumulative risk scores. We also discuss a proof-of-concept implementation of RiskMon as an extension of the Android mobile platform and provide both system evaluation and usability study of our methodology.

Existing online social networks (OSNs) only allow a single user to restrict access to her/his data but cannot provide any mechanism to enforce privacy concerns over data associated with multiple users. This situation leaves privacy conflicts largely unresolved and leads to the potential disclosure of users' sensitive information. To address such an issue, a MultiParty Access Control (MPAC) model was recently proposed, including a systematic approach to identify and resolve privacy conflicts for collaborative data sharing in OSNs. In this paper, we take another step to further study the problem of analyzing the strategic behavior of rational controllers in multiparty access control, where each controller aims to maximize her/his own benefit by adjusting her/his privacy setting in collaborative data sharing in OSNs. We first formulate this problem as a multiparty control game and show the existence of unique Nash Equilibrium (NE) which is critical because at an NE, no controller has any incentive to change her/his privacy setting. We then present algorithms to compute the NE and prove that the system can converge to the NE in only a few iterations. A numerical analysis is also provided for different scenarios that illustrate the interplay of controllers in the multiparty control game. In addition, we conduct user studies of the multiparty control game to explore the gap between game theoretic approaches and real human behaviors.

Mobile Ad hoc Networks (MANET) have been highly vulnerable to attacks due to the dynamic nature of its network infrastructure. Among these attacks, routing attacks have received considerable attention since it could cause the most devastating damage to MANET. Even though there exist several intrusion response techniques to mitigate such critical attacks, existing solutions typically attempt to isolate malicious nodes based on binary or naïve fuzzy response decisions. However, binary responses may result in the unexpected network partition, causing additional damages to the network infrastructure, and naïve fuzzy responses could lead to uncertainty in countering routing attacks in MANET. In this paper, we propose a risk-aware response mechanism to systematically cope with the identified routing attacks. Our risk-aware approach is based on an extended Dempster-Shafer mathematical theory of evidence introducing a notion of importance factors. In addition, our experiments demonstrate the effectiveness of our approach with the consideration of several performance metrics.

Even though promising results have been obtained from existing research on bots and associated command and control channels, there is little research in exploring the ways on how bots are created and distributed by adversaries. Consequently, innovative methods that help determine the linkage between the rogue programs and adversaries are imperative for mitigating and combating botnet attacks. Recent study discovers that rogue programs are sold in black markets in online social networks and adversaries use online social networks to coordinate attacks. Correlation of botnet attacks and activities in online underground social networks is crucial to tactically cope with net-centric threats. In this paper, we take the first step toward adversarial behavior identification by modeling social dynamics of underground adversarial communities and tracing the origin of certain malwares and attack events in underground communities. We also describe our evaluation to demonstrate the effectiveness of our approach.

Mobile Ad hoc Networks (MANET) have been highly vulnerable to attacks due to the dynamic nature of its network infrastructure. Among these attacks, routing attacks have received considerable attention since it could cause the most devastating damage to MANET. Even though there exist several intrusion response techniques to mitigate such critical attacks, existing solutions typically attempt to isolate malicious nodes based on binary or naive fuzzy response decisions. However, binary responses may result in the unexpected network partition, causing additional damages to the network infrastructure, and naive fuzzy responses could lead to uncertainty in countering routing attacks in MANET. In this paper, we propose a risk-aware response mechanism to systematically cope with the identified routing attacks. Our risk-aware approach is based on an extended Dempster-Shafer mathematical theory of evidence introducing a notion of importance factor. In addition, our experiments demonstrate the effectiveness of our approach with the consideration of the packet delivery ratio and routing cost.

Many remote authentication schemes attempt to preserve user anonymity from the eavesdropper. However, authentication schemes in many e-commerce transactions require not only anonymous to the eavesdropper but also to the authentication server. In this paper, we propose a remote authentication scheme using smart card to fulfill both of the requirements. The proposed scheme achieves mutual authentication and allows users to choose and change their own passwords freely and securely. We only use one-way hash function and bitwise XOR operation in the proposed scheme and so the scheme has a low computational complexity. By using timestamp and random numbers, the proposed scheme can resist the denial of service attack and the replay attack. Also, the scheme is secure against guessing attack, insider attack, stolen-verifier attack, reflection attack, and impersonation attack.