Integrated sensing and communication (ISAC) is a cornerstone technology for 6G networks, offering unified support for high-rate communication and high-accuracy sensing. While existing literature extensively covers link-level designs, the transition toward large-scale deployment necessitates a fundamental understanding of network-level performance. This paper investigates a network ISAC model where a source node communicates with a destination via a relay network, while intermediate nodes concurrently perform cooperative sensing over specific spatial regions. We formulate a novel optimization framework that captures the interplay between multi-node routing and sensing coverage. For a one-dimensional path network, we provide an analytical characterization of the complete sensing-throughput region. Extending this to general network topologies, we establish that the sensing-throughput Pareto boundary is piecewise linear and provide physical interpretations for each segment. Our results reveal the fundamental trade-offs between sensing coverage and communication routing, offering key insights for the design of future 6G heterogeneous networks.

The security of wireless challenge-response Physical Layer Authentication (PLA) based on Orthogonal Frequency Division Multiplexing (OFDM) relies on a sufficiently random fading channel condition, which is commonly assumed in existing studies. However, in practical scenarios, such a condition is not always guaranteed and the responses of OFDM subchannels may exhibit correlation. Consequently, ensuring the security of such PLA systems remains an unsolved problem. In this paper, we propose a novel adversary model, called Maximum Differential Likelihood Generator (MDLG), which exploits the weak correlation property in practical wireless channel to launch effective attacks against PLA. Based on this model, we create a measurable guideline using randomness testing to decide when we can in fact use PLA in a practical wireless channel condition. Extensive real-world experiments validate the effectiveness of the MDLG attack and demonstrate how the proposed guideline can help protect the security of PLA.

We present a novel class of content subversion attacks against information-based services, causing documents to appear to humans dissimilar to the underlying content extracted by information-based services. We demonstrate the significant impact of these attacks on real-world systems through five distinct variants. Our first attack allows academic paper writers and reviewers to collude via subverting the automatic reviewer assignment systems in current use by academic conferences including INFOCOM, which we reproduced. Our second attack renders ineffective plagiarism detection software, particularly Turnitin, targeting specific small plagiarism similarity scores to appear natural and evade detection. In our third attack, we place masked content into the indexes for Google, Bing, Yahoo!, and DuckDuckGo, which renders information entirely different from the keywords used to locate it, enabling spam, profane, or possibly illegal content to go unnoticed by these search engines but still returned in unrelated search results. Furthermore, we provide compelling demonstrations of the content subversion attack's efficacy on widely employed QR codes and one-dimensional barcodes. Finally, considering the prevalent avoidance of optical character recognition (OCR) due to computational overhead, we propose a comprehensive and lightweight alternative mitigation method.

The problem of bistatic integrated sensing and communications over memoryless relay channels is considered, where destination concurrently decodes the message sent by the source and estimates unknown parameters from received signals with the help of a relay. A state-dependent discrete memoryless relay channel is considered to model this setup, and the fundamental limits of the communication-sensing performance tradeoff are characterized by the capacity-distortion function. An upper bound on the capacity-distortion function is derived, extending the cut-set bound results to address the sensing operation at the destination. A hybrid-partial-decode-and-compress-forward coding scheme is also proposed to facilitate source-relay cooperation for both message transmission and sensing, establishing a lower bound on the capacity-distortion function. It is found that the hybrid-partial-decode-and-compress-forward scheme achieves optimal sensing performance when the communication task is ignored. Furthermore, the upper and lower bounds are shown to coincide for three specific classes of relay channels. Numerical examples are provided to illustrate the communication-sensing tradeoff and demonstrate the benefits of integrated design.

In the emerging field of Meta Computing, where data collection and integration are essential components, the threat of adversary hidden link attacks poses a significant challenge to web crawlers. In this paper, we investigate the influence of these attacks on data collection by web crawlers, which famously elude conventional detection techniques using large language models (LLMs). Empirically, we find some vulnerabilities in the current crawler mechanisms and large language model detection, especially in code inspection, and propose enhancements that will help mitigate these weaknesses. Our assessment of real-world web pages reveals the prevalence and impact of adversary hidden link attacks, emphasizing the necessity for robust countermeasures. Furthermore, we introduce a mitigation framework that integrates element visual inspection techniques. Our evaluation demonstrates the framework’s efficacy in detecting and addressing these advanced cyber threats within the evolving landscape of Meta Computing.

Recently, adversarial machine learning attacks have posed serious security threats against practical audio signal classification systems, including speech recognition, speaker recognition, music copyright detection. Most existing studies have mainly focused on ensuring the effectiveness of attacking an audio signal classifier via creating a noise-like perturbation on the original signal, which remains a gap in preserving the human perception of adversarial audios. This paper presents a novel perspective to create adversarial audios by integrating the human perception model into the attack formulation to generate well-perceived adversarial examples. Different from conventional approaches which primarily focused on using$L_{p}$norm to preserve the audio quality, we adopt a human study to understand how human participants react to different types of music perturbations, build a Siamese Neural Network (SNN) based model to characterize the human perception. The new findings of the human perception study guide us to formulate a new computationally efficient, multiple-feature-based perception-aware (CEMF-PA) attack, which manipulates different audio signal features to find an optimal perturbed music signal against music copyright detection. This novel attack vector opens a new door to generating highly effective, well-perceived adversarial audio signals via manipulating the auditory features. Experimental results show that the proposed attack is effective against YouTube’s copyright detection. Finally, we propose the defense strategy design to make the copyright detection more robust to adversarial music signals generated by the CEMF-PA attack.

This paper identifies the importance of the safe use of fonts in web and document security. We find multiple attack surfaces that can be exploited by an adversary using malicious fonts. We conduct a comprehensive evaluation of Portable Document Format (PDF) documents collected from the real world to investigate how an attacker can bypass PDF signatures. We further evaluate the potential security threats that an attacker can bring to web-based emails. Our study shows that various security issues may be caused by the inappropriate use of fonts, which are nonethelessly overlooked in the past years. As such, guidelines promoting the secure use of fonts could be beneficial in reinforcing the security measures for digital documents and web pages.

A state-dependent discrete memoryless multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the state parameter sequences through echo signals. In particular, the sensing state parameters are assumed to be correlated with the channel state. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression of past transmitted codewords and echo signals at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate, genie-aided state estimator and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results.

Integrated sensing and communication (ISAC) emerges as a critical technology for future 6G cellular networks. The distinct nature of sensing-centric and communication-centric waveforms poses a challenge, as a unified design can lead to a performance tradeoff between sensing and communication. Most of the previous studies focused on single ISAC base station (BS) scenarios and explored capacity-distortion tradeoffs for various ISAC channels. However, practical scenarios involve multiple ISAC BSs in the same region, sharing time-frequency resources and causing interference, which impacts both sensing and communication functions. To address this challenge, we propose an information-theoretic modeling of monostatic ISAC over interference channels. In the model, two interfering ISAC BSs seek to communicate with their users while performing sensing estimation through received echo signals. An achievable scheme is developed for the considered model, utilizing superposition coding and joint compression of past transmitted codewords and echo signals via distributed Wyner-Ziv coding. The corresponding achievable rate-distortion region is characterized, and a specific example is provided to demonstrate the advantages of the proposed scheme. Our results highlight that interference links, in conjunction with echo signals, can be strategically leveraged to achieve unified cooperative sensing and communication between the two BS-user pairs.

Machine learning has been adopted for efficient cooperative spectrum sensing. However, it incurs an additional security risk due to attacks leveraging adversarial machine learning to create malicious spectrum sensing values to deceive the fusion center, called adversarial spectrum attacks. In this paper, we propose an efficient framework for detecting adversarial spectrum attacks. Our design leverages the concept of the distance to the decision boundary (DDB) observed at the fusion center and compares the training and testing DDB distributions to identify adversarial spectrum attacks. We create a computationally efficient way to compute the DDB for machine learning based spectrum sensing systems. Experimental results based on realistic spectrum data show that our method, under typical settings, achieves a high detection rate of up to 99% and maintains a low false alarm rate of less than 1%. In addition, our method to compute the DDB based on spectrum data achieves 54%–64% improvements in computational efficiency over existing distance calculation methods. The proposed DDB-based detection framework offers a practical and efficient solution for identifying malicious sensing values created by adversarial spectrum attacks.

A relay-aided bistatic integrated sensing and communication (ISAC) system is considered, where the destination concurrently decodes a message and estimates unknown state parameters from its received signals. This system is modeled by a generalized state-dependent relay channel. Its fundamental limits of the communication-sensing performance tradeoff, characterized by the capacity-distortion function, are established. Specifically, an upper bound on the capacity-distortion function extending the cutset bound for relay channels to address the state estimation at the destination is developed. Additionally, a hybrid partial-decode-and-compress-forward coding scheme is proposed to facilitate source-relay cooperation for both message transmission and state estimation, establishing a lower bound on the capacity-distortion function. It is found that partial-decode-and-compress-forward scheme achieves optimal sensing performance when the communication task is ignored. Furthermore, the upper and lower bounds are shown to coincide for some special classes of channels. Two numerical examples are provided to illustrate the communication-sensing tradeoff in the considered ISAC system.

The channel state information (CSI) has been extensively studied in the literature to facilitate authentication in wireless networks. The less focused is a systematic attack model to evaluate CSI-based authentication. Existing studies generally adopt either a random attack model that existing designs are resilient to or a specific-knowledge model that assumes certain inside knowledge for the attacker. This paper proposes a new, realistic attack model against CSI-based authentication. In this model, an attacker Eve tries to actively guess a user Alice’s CSI, and precode her signals to impersonate Alice to the verifier Bob who uses CSI to authenticate users. To make the CSI guessing effective and low-cost, we use theoretical analysis and CSI dataset validation to show that there is no need to guess CSI values in all signal propagation paths. Specifically, Eve can adopt a Dominant Path Construction (DomPathCon) strategy that only focuses on guessing the CSI values on the first few paths with the highest channel response amplitude (called dominant paths). Comprehensive experimental results show that DomPathCon is effective and achieves up to 61% attack success rates under different wireless network settings, which exposes new limitations of CSI-based authentication. We also propose designs to mitigate the adverse impact of DomPathCon.

Location spoofing attack deceiving a Wi-Fi positioning system has been studied for over a decade. However, it has been challenging to construct a practical spoofing attack in urban areas with dense coverage of legitimate Wi-Fi APs. This paper identifies the vulnerability of the Google Geolocation API, which returns the location of a mobile device based on the information of the Wi-Fi access points that the device can detect. We show that this vulnerability can be exploited by the attacker to reveal the black-box localization algorithms adopted by the Google Wi-Fi positioning system and easily launch the location spoofing attack in dense urban areas with a high success rate. Furthermore, we find that this vulnerability can also lead to severe consequences that hurt user privacy, including the leakage of sensitive information like precise locations, daily activities, and demographics. Ultimately, we discuss the potential countermeasures that may be used to mitigate this vulnerability and location spoofing attack.

The continuous integration and continuous deployment (CI/CD) pipeline has been widely used and is becoming popular on Internet hosting platforms, such as GitHub. While being popular, however, current CI/CD pipelines suffer from malicious code and severe vulnerabilities. Even worse, it is often under-protected as people have not been fully aware of its attack surfaces and the corresponding impacts. Therefore, in this paper, we conduct a large-scale measurement and a systematic analysis to reveal the attack surfaces of the CI/CD pipeline and quantify their security impacts. Specifically, for the measurement, we collect a data set of 320,000+ CI/CD pipeline-configured GitHub repositories and build an analysis tool to parse the CI/CD pipelines and extract security-critical usages. Our measurement reveals that the script runtimes are prone to code hiding while the script usage update is not in time, giving attackers chances to hide malicious code and exploit existing vulnerabilities. Moreover, even the scripts from verified creators may contain severe vulnerabilities. Besides current CI/CD ecosystem heavily relies on several core scripts, which may lead to a single point of failure. While the CI/CD pipelines contain sensitive information/operations, making them the attacker's favorite targets. Inspired by the measurement findings, we abstract the threat model and the attack approach toward CI/CD pipelines, followed by a systematic analysis of attack surfaces, attack strategies, and the corresponding impacts. We further launch case studies on five attacks in real-world CI/CD environments to validate the revealed attack surfaces. Finally, we give suggestions on mitigating attacks on CI/CD scripts, including securing CI/CD configurations, securing CI/CD scripts, and improving CI/CD infrastructure.

Facial recognition technology has been developed and widely used for decades. However, it has also made privacy concerns and researchers’ expectations for facial recognition privacy-preserving technologies. To provide privacy, detailed or semantic contents in face images should be obfuscated. However, face recognition algorithms have to be tailor-designed according to current obfuscation methods, as a result the face recognition service provider has to update its commercial off-the-shelf (COTS) products for each obfuscation method. Meanwhile, current obfuscation methods have no clearly quantified explanation. This paper presents a universal face obfuscation method for a family of face recognition algorithms using global or local structure of eigenvector space. By specific mathematical explanations, we show that the upper bound of the distance between the original and obfuscated face images is smaller than the given recognition threshold. Experiments show that the recognition degradation is 0% for global structure based and 0.3%-5.3% for local structure based, respectively. Meanwhile, we show that even if an attacker knows the whole obfuscation method, he/she has to enumerate all the possible roots of a polynomial with an obfuscation coefficient, which is computationally infeasible to reconstruct original faces. So our method shows a good performance in both privacy and recognition accuracy without modifying recognition algorithms.

Wireless cameras nowadays commonly employ motion sensors to identify that something is occurring in their fields of vision before starting to record and notifying the property owner of the activity. In this paper, we discover that the motion sensing action can disclose the location of the camera through a novel wireless camera localization technique we call MotionCompass. By creating motion stimuli and sniffing wireless traffic for a response to that stimuli, a user can obtain the motion trajectories within the motion detection zone and then use them to calculate the camera's location. We also extend the camera localization algorithm to pinpoint cameras in always-active mode. We develop an Android app to implement MotionCompass. Our extensive experiments using the developed app and 18 popular wireless cameras demonstrate that for cameras with one motion sensor, MotionCompass can attain a mean localization error of around 5 cm with less than 140 seconds. We also discuss defenses against MotionCompass. Our localization technique builds upon existing work that detects the existence of hidden cameras, to pinpoint their exact location.

Federated learning (FL) trains a global learning model by using a central server to collaborate with multiple decentralized clients. In a wireless network, the data transmission latency between a client and the FL server is substantially affected by signal quality dynamics and bandwidth allocation. FL clients require synchronized communication at each round to update their models simultaneously, which makes bandwidth allocation methods for conventional wireless tasks infeasible to use. Existing bandwidth allocation studies for FL mainly focused on allocating bandwidth of one bandwidth provider without cost. In this paper, we consider a more practical and challenging problem: how to assign the bandwidth to clients under multiple wireless providers to minimize the FL round length (i.e., the latency that FL finishes one round of model training and updating) with bandwidth capability and cost constraints? We propose a model that maps the problem into a new variant of the knapsack problem, called multi-dimensional max-min multiple knapsacks (MDM$^{\,3}$KP). Based on MDM$^{\,3}$KP, we create an iterative solution to find the client assignment and bandwidth allocation that minimizes the FL round length. Comprehensive simulation results show that the solution reduces the FL round length by up to 70.8% compared with other benchmarks.

We debut the Warmonger attack, a novel attack vector that can cause denial-of-service between a serverless computing platform and an external content server. The Warmonger attack exploits the fact that a serverless computing platform shares the same set of egress IPs among all serverless functions, which belong to different users, to access an external content server. As a result, a malicious user on this platform can purposefully misbehave and cause these egress IPs to be blocked by the content server, resulting in a platform-wide denial of service. To validate the effectiveness of the Warmonger attack, we conducted extensive experiments over several months, collecting and analyzing the egress IP usage patterns of five prominent serverless service providers (SSPs): Amazon Web Service (AWS) Lambda, Google App Engine, Microsoft Azure Functions, Cloudflare Workers, and Alibaba Function Compute. Additionally, we conducted a thorough evaluation of the attacker’s potential actions to compromise an external server and trigger IP blocking. Our findings revealed that certain SSPs employ surprisingly small sets of egress IPs, sometimes as few as four, which are shared among their user base. Furthermore, our research demonstrates that the serverless platform offers ample opportunities for malicious users to engage in well-known disruptive behaviors, ultimately resulting in IP blocking. Our study uncovers a significant security threat within the burgeoning serverless computing platform and sheds light on potential mitigation strategies, such as the detection of malicious serverless functions and the isolation of such entities.

Wireless security cameras may deter intruders. Accompanying the hardware, consumers may pay recurring monthly fees for recording videos to the cloud, or use the free tier offering motion alerts and sometimes live streams via the camera app. Many users may purchase the hardware without buying the subscription to save money, which inherently reduces their efficacy. We discover that the wireless traffic generated by a camera responding to stimulating motion may disclose whether or not video is being streamed. A malicious user such as a burglar may use such knowledge to target homes with a ''weak camera'' that does not upload video or turn on live view mode. In such cases, criminal activities would not be recorded though they are performed within the monitoring area of the camera. Accordingly, we describe a novel technique called WeakCamID that creates motion stimuli and sniffs resultant wireless traffic to infer the camera state. We perform a survey involving a total of 220 users, finding that all users think cameras have a consistent security guarantee regardless of the subscription status. Our discovery breaks such ''common sense''. We implement WeakCamID in a mobile app and experiment with 11 popular wireless cameras to show that WeakCamID can identify weak cameras with a mean accuracy of around 95% and within less than 19 seconds.

Integrated sensing and communication (ISAC) is a promising technology for future 6G networks that enables the joint utilization of hardware and spectrum resources for sensing and communication systems. However, the co-sharing of resources leads to a fundamental tradeoff between sensing and communication performance, which is not well understood in multiple-access ISAC scenarios with perfect or imperfect channel state information at the receiver (CSIR). In this paper, we address this challenge by considering a state-dependent multiple access channel model that accounts for correlated sensing and channel states, as well as imperfect CSIR. We propose an achievable scheme that combines message cooperation and joint compression via distributed Wyner-Ziv coding at each user, resulting in unified cooperative communication and sensing. Our scheme always achieves a communication-rate-distortion region which includes that achieved by state-of-the-art coding scheme. In addition, a numerical example is provided to demonstrate strict inclusion. It is found that the compressed information not only enhances communication (especially in scenarios with imperfect CSIR) but also improves sensing performance.

New network architectures, such as the Internet of Things (IoT), 5G, and next-generation (NextG) cellular systems, put forward emerging challenges to the design of future wireless networks toward ultra-high data rate, massive data processing, smart designs, low-cost deployment, reliability and security in dynamic environments. As one of the most promising techniques today, artificial intelligence (AI) is advocated to enable a data-driven paradigm for wireless network design. In this paper, we are motivated to review existing AI techniques and their applications for the full wireless network protocol stack toward improving network performance and security. Our goal is to summarize the current motivation, challenges, and methodology of using AI to enhance wireless networking from the physical to the application layer, and shed light on creating new AI-enabled algorithms, mechanisms, protocols, and system designs for future data-driven wireless networking.

Due to the open nature of the wireless medium, wireless communications are especially vulnerable to eavesdropping attacks. This article designs a new wireless communication system to deal with eavesdropping attacks. The proposed system can enable a legitimate receiver to get desired messages and meanwhile an eavesdropper to hear “fake” but meaningful messages by combining confidentiality and deception, thereby confusing the eavesdropper and achieving additional concealment that further protects exchanged messages. Towards this goal, we propose techniques that can conceal exchanged messages by utilizing wireless channel characteristics between the transmitter and the receiver, as well as techniques that can attract an eavesdropper to gradually approach a trap region, where the eavesdropper can get fake messages. We also provide both theoretical and empirical analysis of the established secure channel between the transmitter and the receiver. We develop a prototype system using Universal Software Defined Radio Peripherals (USRPs). Experimental results show that an eavesdropper at a trap location can receive fake information with a bit error rate (BER) close to 0, and the transmitter with multiple antennas can successfully deploy a trap area.

Federated learning (FL) provides a high efficient decentralized machine learning framework, where the training data remains distributed at remote clients in a network. Though FL enables a privacy-preserving mobile edge computing framework using IoT devices, recent studies have shown that this approach is susceptible to poisoning attacks from the side of remote clients. To address the poisoning attacks on FL, we provide atwo-phasedefense algorithm called${\underline{Lo}cal\ \underline{Ma}licious\ Facto\underline{r}}$(LoMar). In phase I, LoMar scores model updates from each remote client by measuring the relative distribution over their neighbors using a kernel density estimation method. In phase II, an optimal threshold is approximated to distinguish malicious and clean updates from a statistical perspective. Comprehensive experiments on four real-world datasets have been conducted, and the experimental results show that our defense strategy can effectively protect the FL system. Specifically, the defense performance on Amazon dataset under a label-flipping attack indicates that, compared with FG+Krum, LoMar increases the target label testing accuracy from$96.0\%$to$98.8\%$, and the overall averaged testing accuracy from$90.1\%$to$97.0\%$.

Multi-server Federated learning (FL) has been considered as a promising solution to address the limited communication resource problem of single-server FL. We consider a typical multi-server FL architecture, where the coverage areas of regional servers may overlap. The key point of this architecture is that the clients located in the overlapping areas update their local models based on the average model of all accessible regional models, which enables indirect model sharing among different regional servers. Due to the complicated network topology, the convergence analysis is much more challenging than in single-server FL. In this paper, we firstly propose a novel MS-FedAvg algorithm for this multi-server FL architecture and analyze its convergence on non-iid datasets for general non-convex settings. Since the number of clients located in each regional server is much less than single-server FL, the bandwidth of each client should be large enough to successfully communicate training models with the server, which indicates that full client participation can work in multi-server FL. Also, we provide the convergence analysis of the partial client participation scheme and develop a new biased partial participation strategy to further accelerate convergence. Our results indicate that the convergence results highly depend on the ratio of the number of clients in each area type to the total number of clients in all three strategies. The extensive experiments show remarkable performance and support our theoretical results.

Previous adversarial audio attacks have mainly focused on ensuring the effectiveness of attacking an audio signal classifier via creating a small noise-like perturbation on the original signal. It is still unclear if an attacker is able to create audio signal perturbations that can be well perceived by human beings in addition to its attack effectiveness. In this work, we formulate the adversarial attack against music signals as a new perception-aware attack framework, which integrates human study into adversarial attack design. Specifically, we invite human participants to rate their perceived deviation based on pairs of original and perturbed music signals, and reverse-engineer the human perception process by regression analysis to predict the human-perceived deviation given a perturbed signal. The perception-aware attack is then formulated as an optimization problem that finds an optimal perturbation signal to minimize the prediction of perceived deviation from the regressed human perception model. Experiments show that the attack produces adversarial music with significantly better perceptual quality than prior work against YouTube's copyright detector.

Location spoofing attack deceiving a Wi-Fi positioning system has been studied for over a decade. However, it has been challenging to construct a practical spoofing attack in urban areas with dense coverage of legitimate Wi-Fi APs. This paper identifies the vulnerability of the Google Geolocation API, which returns the location of a mobile device based on the information of the Wi-Fi access points that the device can detect. We show that this vulnerability can be exploited by the attacker to reveal the black-box localization algorithms adopted by the Google Wi-Fi positioning system and easily launch the location spoofing attack in dense urban areas with a high success rate. Furthermore, we find that this vulnerability can also lead to severe consequences that hurt user privacy, including the leakage of sensitive information like precise locations, daily activities, and demographics. Ultimately, we discuss the potential countermeasures that may be used to mitigate this vulnerability and location spoofing attack.

Federated Learning (FL) is a promising framework for performing privacy-preserving, distributed learning with a set of clients. However, the data distribution among clients often exhibits non-IID, i.e., distribution shift, which makes efficient optimization difficult. To tackle this problem, many FL algorithms focus on mitigating the effects of data heterogeneity across clients by increasing the performance of the global model. However, almost all algorithms leverage Empirical Risk Minimization (ERM) to be the local optimizer, which is easy to make the global model fall into a sharp valley and increase a large deviation of parts of local clients. Therefore, in this paper, we revisit the solutions to the distribution shift problem in FL with a focus on local learning generality. To this end, we propose a general, effective algorithm, \texttt{FedSAM}, based on Sharpness Aware Minimization (SAM) local optimizer, and develop a momentum FL algorithm to bridge local and global models, \texttt{MoFedSAM}. Theoretically, we show the convergence analysis of these two algorithms and demonstrate the generalization bound of \texttt{FedSAM}. Empirically, our proposed algorithms substantially outperform existing FL studies and significantly decrease the learning deviation.

WiFi 5/6 relies on a key feature, Multi-User Multiple-In-Multiple-Out (MU-MIMO), to offer high-volume network throughput and spectrum efficiency. MU-MIMO uses a user selection algorithm, based on each user's channel state information (CSI), to schedule transmission opportunities for a group of users to maximize the service quality and efficiency. In this paper, we discover that such algorithm creates a subtle attack surface for attackers to subvert user selection in MU-MIMO, causing severe disruptions in today's wireless networks. We develop a system, named MU-MIMO user selection strategy inference and subversion (MUSTER), to systematically study the attack strategies and further to seek efficient mitigation. MUSTER is designed to include two major modules: (i) strategy inference, which leverages a new neural group-learning strategy named MC-grouping via combining Recurrent Neural Network (RNN) and Monte Carlo Tree Search (MCTS) to reverseengineer a user selection algorithm, and (ii) user selection subversion, which proactively fabricates CSI to manipulate user selection results for disruption. Experimental evaluation shows that MUSTER achieves a high accuracy rate around 98.6% in user selection prediction and effectively launches the attacks to disrupt the network performance. Finally, we create a Reciprocal Consistency Checking technique to defend against the proposed attacks to secure MU-MIMO user selection.

As Internet of Things (IoT) is more and more pervasive and deployed in critical applications, it's becoming increasingly important to preserve the confidentiality of sensitive data when IoT devices communicate with each other. However, traditional cryptography is usually time and energy consuming. It may not be applicable to IoT devices with limited computational capability or limited power. In this paper, we propose a lightweight encryption scheme named Dynamic Wireless Channel P ad (DyWCP) inspired by one-time pad encryption. One-time pad encryption achieves perfect secrecy but has been rarely used in practice due to the inconvenience of key negotiation. Our research discovers that in the wireless context it is possible to design a one-time pad encryption scheme without key negotiation. Towards the realization of DyWCP, we create techniques to utilize the additive feature of wireless channel to encrypt messages, to integrate modular operations at wireless physical layer, and to defend against multiple eavesdroppers. We implement a prototype of the proposed scheme using Universal Software Defined Radio Peripherals (USRP), and conduct a suite of experiments to evaluate the performance of the proposed scheme.

Program control flow reflects the algorithm of that program and may reveal implementation vulnerabilities. Thus its confidentiality needs to be protected, especially in a cloud setting. However, most existing control flow obfuscation methods are software-based, which cannot offer high confidentiality while maintaining low performance overhead. In this paper, we propose CFHider, a hardware-assisted solution. By performing program transformation and leveraging Trusted Execution Environments (Intel SGX), CFHider moves branch statement conditions to an opaque and trusted memory space during the program execution. We proved that by generating Obfuscation Invariants, CFHider is able to provide provable control flow confidentiality protection. Based on the design of CFHider, we also developed a prototype system for Java applications. Our security analysis and experimental results indicate that CFHider is effective in protecting control flow confidentiality and incurs a much reduced performance overhead than existing software-based solutions (by a factor of 18.1).

Existing research work has identified a new class of attacks that can eavesdrop on the keystrokes in a non-invasive way without infecting the target computer to install malware. The common idea is that pressing a key of a keyboard can cause a unique and subtle environmental change, which can be captured and analyzed by the eavesdropper to learn the keystrokes. For these attacks, however, a training phase must be accomplished to establish the relationship between an observed environmental change and the action of pressing a specific key. This significantly limits the impact and practicality of these attacks. In this paper, we discover that it is possible to design keystroke eavesdropping attacks without requiring the training phase. We create this attack based on the channel state information extracted from the wireless signal. To eavesdrop on keystrokes, we establish a mapping between typing each letter and its respective environmental change by exploiting the correlation among observed changes and known structures of dictionary words. To defend against this attack, we propose a reactive jamming mechanism that launches the jamming only during the typing period. Experimental results on software-defined radio platforms validate the impact of the attack and the performance of the defense.

Federated Learning (FL) has been considered as an appealing framework to tackle data privacy issues of mobile devices compared to conventional Machine Learning (ML). Using Edge Servers (ESs) as intermediaries to perform model aggregation in proximity can reduce the transmission overhead, and it enables great potential in low-latency FL, where the hierarchical architecture of FL (HFL) has been attracted more attention. Designing a proper client selection policy can significantly improve training performance, and it has been widely investigated in conventional FL studies. However, to the best of our knowledge, systematic client selection policies have not yet been fully studied for HFL. In addition, client selection for HFL faces more challenges than conventional FL (e.g., the time-varying connection of client-ES pairs and the limited budget of the Network Operator (NO)). In this article, we investigate a client selection problem for HFL, where the NO learns the number of successful participating clients to improve training performance (i.e., select as many clients in each round) as well as under the limited budget on each ES. An online policy, called Context-aware Online Client Selection (COCS), is developed based on Contextual Combinatorial Multi-Armed Bandit (CC-MAB). COCS observes the side-information (context) of local computing and transmission of client-ES pairs and makes client selection decisions to maximize NO's utility given a limited budget. Theoretically, COCS achieves a sublinear regret compared to an Oracle policy on both strongly convex and non-convex HFL. Simulation results also support the efficiency of the proposed COCS policy on real-world datasets.

We debut the Warmonger attack, a novel attack vector that can cause denial-of-service between a serverless computing platform and an external content server. The Warmonger attack exploits the fact that a serverless computing platform shares the same set of egress IPs among all serverless functions, which belong to different users, to access an external content server. As a result, a malicious user on this platform can purposefully misbehave and cause these egress IPs to be blocked by the content server, resulting in a platform-wide denial of service. To validate the Warmonger attack, we ran months-long experiments, collected and analyzed the egress IP usage pattern of four major serverless service providers (SSPs). We also conducted an in-depth evaluation of an attacker's possible moves to inflict an external server and cause IP-blockage. We demonstrate that some SSPs use surprisingly small numbers of egress IPs (as little as only four) and share them among their users, and that the serverless platform provides sufficient leverage for a malicious user to conduct well-known misbehaviors and cause IP-blockage. Our study unveiled a potential security threat on the emerging serverless computing platform, and shed light on potential mitigation approaches.

Wireless security cameras are integral components of security systems used by military installations, corporations, and, due to their increased affordability, many private homes. These cameras commonly employ motion sensors to identify that something is occurring in their fields of vision before starting to record and notifying the property owner of the activity. In this paper, we discover that the motion sensing action can disclose the location of the camera through a novel wireless camera localization technique we call MotionCompass. In short, a user who aims to avoid surveillance can find a hidden camera by creating motion stimuli and sniffing wireless traffic for a response to that stimuli. With the motion trajectories within the motion detection zone, the exact location of the camera can be then computed. We develop an Android app to implement MotionCompass. Our extensive experiments using the developed app and 18 popular wireless security cameras demonstrate that for cameras with one motion sensor, MotionCompass can attain a mean localization error of around 5 cm with less than 140 seconds. This localization technique builds upon existing work that detects the existence of hidden cameras, to pinpoint their exact location and area of surveillance.

Voice interface has been a dominant User Interface (UI) channel in the popular smart home environment. Although Voice Control System (VCS) brings users conveniences, it is extremely vulnerable to spoofing attacks (e.g., hidden/inaudible command attack) due to its broadcast nature. In this study, to thwart spoofing attacks, we propose WSVA, a device-free voice liveness detection system based on the prevalent wireless signals generated by IoT devices without requiring user to carry any additional sensor or device. The basic insight of WSVA to distinguish the authentic voice command from a spoofed one is checking the consistency between the voice signal and its corresponding mouth motions, which can be captured by wireless signals. To achieve this goal, WSVA builds a theoretical model to describe the correlations among the wireless signal changes, the mouth motions, and the syllables in the voice command. Then, WSVA selects appropriate features from both voice and wireless signals, and calculates the consistency between these two types of signals to determine whether the VCS is suffering from the spoofing attack. To demonstrate the feasibility of WSVA, we conduct a case study on Samsung SmartThings platform and include WSVA as a new application, which is expected to significantly enhance the security of the existing VCS. We evaluate WSVA with various voice commands in different scenarios. Experimental results demonstrate that WSVA achieves the overall 99 percent true accept rate with 1 percent false accept rate with a good scalability and low latency.

As wireless mobile devices are more and more pervasive and adopted in critical applications, it is becoming increasingly important to measure the physical proximity of these devices in a secure way. Although various techniques have been developed to identify whether a device is close, the problem of identifying the far proximity (i.e., a target is at least a certain distance away) has been neglected by the research community. Meanwhile, verifying the far proximity is desirable and critical to enhance the security of emerging wireless applications. In this article, we propose a secure far proximity identification approach that determines whether or not a remote device is far away. The key idea of the proposed approach is to estimate the far proximity from the unforgeable “fingerprint” of the proximity. We have validated and evaluated the effectiveness of the proposed far proximity identification method through experiments on real measured channel data. The experiment results show that the proposed approach can detect the far proximity with a successful rate of 0.85 for the non-Line-of-sight (NLoS) scenario, and the successful rate can be further increased to 0.99 for the Line-of-sight (LoS) scenario.

We revisit the traditional framework of wireless secret key generation, where two parties leverage the wireless channel randomness to establish a secret key. The essence in the framework is to quantify channel randomness into bit sequences for key generation. Conducting randomness tests on such bit sequences has been a common practice to provide the confidence to validate whether they are random. Interestingly, despite different settings in the tests, existing studies interpret the results the same: passing tests means that the bit sequences are indeed random. In this paper, we investigate how to properly test the wireless channel randomness to ensure enough security strength and key generation efficiency. In particular, we define an adversary model that leverages the imperfect randomness of the wireless channel to search the generated key, and create a guideline to set up randomness testing and privacy amplification to eliminate security loss and achieve efficient key generation rate. We use theoretical analysis and comprehensive experiments to reveal that common practice misuses randomness testing and privacy amplification: (i) no security insurance of key strength, (ii) low efficiency of key generation rate. After revision by our guideline, security loss can be eliminated and key generation rate can be increased significantly.

Wireless communication is easily eavesdropped due to the broadcast nature of the wireless medium. This has spurred extensive research into secret key establishment using physical layer characteristics of wireless channels. In all these schemes, the secret keys directly originate from the physical features of the real wireless channel, which is highly dependent on the communication environment nearby. Also, previous schemes require performing information reconciliation, which increases both the costs and the risk of key leakage. In this paper, we exhibit a novel wireless key establishment method allowing the transmitter to specify arbitrary content as the key and cause the receiver to obtain the same key leveraging a channel manipulation technique. We furthermore enable the transmitter to apply error-correction code to the key, so that the receiver can automatically correct any mismatched bits without sending key-related information back to the transmitter over the public channel. Experimental results demonstrate that our key establishment method reaches a success rate as high as 91.0 percent for establishing a 168-bit key between the transmitter and the receiver, and meanwhile the chance that the eavesdropper can infer the key in meter-order range of the receiver is subdued into the range of 0~0.10 percent.

The topology of a network is fundamental for building network infrastructure functionalities. In many scenarios, enterprise networks may have no desire to disclose their topology information. In this paper, we aim at preventing attacks that use adversarial, active end-to-end topology inference to obtain the topology information of a target network. To this end, we propose a Proactive Topology Obfuscation (ProTO) system that adopts a detect-then-obfuscate framework: (i) a lightweight probing behavior identification mechanism based on machine learning is designed to detect any probing behavior, and then (ii) a topology obfuscation design is developed to proactively delay all identified probe packets in a way such that the attacker will obtain a structurally accurate yet fake network topology based on the measurements of these delayed probe packets, therefore deceiving the attacker and decreasing its appetency for future inference. We evaluate ProTO under different evaluation scenarios. Experimental results show that ProTO is able to (i) achieve a detection rate of 99.9% with a false alarm of 3%, (ii) effectively disrupt adversarial topology inference and lead to the topology inferred by the attacker close to a fake topology, and (iii) result in an overall network delay performance degradation of 1.3% - 2.0%.

The topology of a network is fundamental for building network infrastructure functionalities. In many scenarios, enterprise networks may have no desire to disclose their topology information. In this paper, we aim at preventing attacks that use adversarial, active end-to-end topology inference to obtain the topology information of a target network. To this end, we propose a Proactive Topology Obfuscation (ProTO) system that adopts a detect-then-obfuscate framework: (i) a lightweight probing behavior identification mechanism based on machine learning is designed to detect any probing behavior, and then (ii) a topology obfuscation design is developed to proactively delay all identified probe packets in a way such that the attacker will obtain a structurally accurate yet fake network topology based on the measurements of these delayed probe packets, therefore deceiving the attacker and decreasing its appetency for future inference. We show that ProTO is very effective against active topology inference with minimum performance disruption. Experimental results under different evaluation scenarios show that ProTO is able to (i) achieve a detection rate of 99.9% with a false alarm of 3%, (ii) effectively disrupt adversarial topology inference and lead to the topology inferred by the attacker close to a fake topology, and (iii) result in an overall network delay performance degradation of 1.3% - 2.0%.

In this study, we present WindTalker, a novel and practical keystroke inference framework that can be used to infer the sensitive keystrokes on a mobile device through WiFi-based side-channel information. WindTalker is motivated from an observation that keystrokes on mobile devices will lead to different hand coverage and the finger motions, which will introduce a unique interference to the multi-path signals and can be reflected by the channel state information (CSI). An attacker can exploit the strong correlation between the CSI fluctuation and the keystrokes to infer the user's password input. Compared with the previous keystroke inference approaches, WindTalker neither deploys external equipment physically close to the target device nor compromises the target device. Instead, it employs a more practical setting by deploying a free public WiFi hotspot and collects the CSI data from the target device as long as the device is connected to the hotspot. In addition, to improve inference accuracy and efficiency, it analyzes the WiFi traffic to selectively collect CSI only for the sensitive period where password entering occurs. WindTalker can be implemented without the requirement of visually seeing the target device, or installing any malware on the device. We tested Windtalker on several mobile phones and performed a detailed case study to evaluate the practicality of the password inference towards Alipay, the largest mobile payment platform in the world. Furthermore, we proposed a novel CSI obfuscation countermeasure to thwart the inference attack. The evaluation results show that the performance of WindTalker can be dramatically reduced by adopting the proposed countermeasures.

Due to the open nature of wireless medium, wireless communications are especially vulnerable to eavesdropping attacks. This paper designs a new wireless communication system to deal with eavesdropping attacks. The proposed system can enable a legitimate receiver to get desired messages and meanwhile an eavesdropper to hear “fake” but meaningful messages, thereby confusing the eavesdropper and achieving additional concealment that further protects exchanged messages. Towards this goal, we propose techniques that can conceal exchanged messages by utilizing wireless channel characteristics between the transmitter and the receiver, as well as techniques that can attract an eavesdropper to gradually approach a trap region, where the eavesdropper can get fake messages. We also implement and evaluate the proposed system on top of Universal Software Defined Radio Peripherals (USRPs). Experimental results show that an eavesdropper at a trap location can receive fake information with a bit error rate (BER) that is close to 0, and the transmitter with multiple antennas can successfully deploy a trap area.

When a program is executed on an untrusted cloud, the confidentiality of the program's logics needs to be protected. Control flow obfuscation is a direct approach to obtain this goal. However, existing methods in this direction cannot achieve both high confidentiality and low overhead. In this paper, we propose CFHider, a hardware-assisted method to protect the control flow confidentiality. By combining program transformation and Intel Software Guard Extension (SGX) technology, CFHider moves branch statement conditions to an opaque and trusted memory space, i.e., the enclave, thereby offering a guaranteed control flow confidentiality. Based on the design of CFHider, we developed a prototype system targeting on Java applications. Our analysis and experimental results indicate that CFHider is effective in protecting the control flow confidentiality and incurs a much reduced performance overhead than existing software-based solutions (by a factor of 8.8).

Wireless jamming remains as one of the primary threats towards wireless security. Traditionally, jamming is able to disrupt wireless signals within, but not beyond, its covered bandwidth. In this paper, we propose a novel attack strategy, called orthogonality-sabotaging attack, against orthogonal frequency division multiple access (OFDMA) that has been widely adopted in today's wireless network standards (e.g., 4G/5G and 802.11ax). The attack intentionally introduces an unaligned narrowband jamming signal to an OFDMA network so as to destroy the orthogonality among all subcarriers in broadband signals. We theoretically formulate and optimize the attack strategies, and then use real-world experiments to show that orthogonality sabotaging is very efficient and can take down an 802.11ax network with only 1/5-1/4 of the full network bandwidth. Finally, we propose an attack identification and localization method to identify and localize orthogonality-sabotaging attacks in the fullband spectrum with 92% overall accuracy and localization errors within about 0.4 subcarrier spacing in experiments.

The prevalence and monetary value of mobile devices, coupled with their compact and, indeed, mobile nature, lead to frequent theft due to a lack of proper anti-theft mechanisms. Currently, there only exist damage control efforts such as remote wiping the device's memory or GPS tracking, but nothing to notify users of theft while it takes place. We propose such a mechanism which utilizes the unique walking patterns inherent to humans and differentiate our work from other walking behavior studies by using it as first-order authentication and developing matching methods fast enough to act as an actual anti-theft system. We test our system with the aid of 45 volunteers and demonstrate detection of unauthorized movement within 10 to 20 steps with an accuracy of 96.4 to 98.4 percent, while simultaneously distinguishing owners as themselves with 97.8 percent accuracy.

Traditional methods to eavesdrop keystrokes leverage some malware installed in a target computer to record the keystrokes for an adversary. Existing research work has identified a new class of attacks that can eavesdrop the keystrokes in a non-invasive way without infecting the target computer to install a malware. The common idea is that pressing a key of a keyboard can cause a unique and subtle environmental change, which can be captured and analyzed by the eavesdropper to learn the keystrokes. For these attacks, however, a training phase must be accomplished to establish the relationship between an observed environmental change and the action of pressing a specific key. This significantly limits the impact and practicality of these attacks. In this paper, we discover that it is possible to design keystroke eavesdropping attacks without requiring the training phase. We create this attack based on the channel state information extracted from wireless signal. To eavesdrop keystrokes, we establish a mapping between typing each letter and its respective environmental change by exploiting the correlation among observed changes and known structures of dictionary words. We implement this attack on software-defined radio platforms and conduct a suite of experiments to validate the impact of this attack. We point out that this paper does not propose to use wireless signal for inferring keystrokes, since such work already exists. Instead, the main goal of this paper is to propose new techniques to remove the training process, which can make existing work unpractical.

With the prevalent of smart devices and home automations, voice command has become a popular User Interface (UI) channel in the IoT environment. Although Voice Control System (VCS) has the advantages of great convenience, it is extremely vulnerable to the spoofing attack (e.g., replay attack, hidden/inaudible command attack) due to its broadcast nature. In this study, we present WiVo, a device-free voice liveness detection system based on the prevalent wireless signals generated by IoT devices without any additional devices or sensors carried by the users. The basic motivation of WiVo is to distinguish the authentic voice command from a spoofed one via its corresponding mouth motions, which can be captured and recognized by wireless signals. To achieve this goal, WiVo builds a theoretical model to characterize the correlation between wireless signal dynamics and the user's voice syllables. WiVo extracts the unique features from both voice and wireless signals, and then calculates the consistency between these different types of signals in order to determine whether the voice command is generated by the authentic user of VCS or an adversary. To evaluate the effectiveness of WiVo, we build a testbed based on Samsung SmartThings framework and include WiVo as a new application, which is expected to significantly enhance the security of the existing VCS. We have evaluated WiVo with 6 participants and different voice commands. Experimental evaluation results demonstrate that WiVo achieves the overall 99% detection rate with 1% false accept rate and has a low latency.

We propose a novel wireless technique named pinpoint waveforming to achieve the location-restricted service access control, i.e., providing wireless services to users at eligible locations only. The proposed system is inspired by the fact that when two identical wireless signals arrive at a receiver simultaneously, they will constructively interfere with each other to form a boosted signal whose amplitude is twice of that of an individual signal. As such, the location-restricted service access control can be achieved through transmitting at a weak power, so that receivers at undesired locations (where the constructive interference vanishes), will experience a low signal-to-noise ratio (SNR), and hence a high bit error rate that retards the correct decoding of received messages. At the desired location (where the constructive interference happens), the receiver obtains a boosted SNR that enables the correct message decoding. To solve the difficulty of determining an appropriate transmit power, we propose to entangle the original transmit signals with jamming signals of opposite phase. The jamming signals can significantly reduce the SNR at the undesired receivers but cancel each other at the desired receiver to cause no impact. With the jamming entanglement, the transmit power can be any value specified by the system administrator. To enable the jamming entanglement, we create the channel calibration technique that allows the synchronization of transmit signals at the desired location. We develop a prototype system using the Universal Software Defined Radio Peripherals (USRPs). The evaluation results show that the receiver at the desired location obtains a throughput ranging between 0.9 and 0.93, whereas an eavesdropper that is 0.3 meter away from a desired location has a throughput approximately equal to 0.

Recently, people have witnessed a remarkable growth in the number of smart wearable devices. Accompanied with the development of a contactless data transmission technique, the lack of effective secret key establishment between lightweight wearable devices which support contactless data transmission technique becomes a security bottleneck. In this paper, we propose a novel wireless key establishment method by moving or shaking the wearable wireless devices. Instead of received signal strength (RSS) itself, we denote the RSS trajectories of two moving wireless devices as the materials of secret key. Moreover, inspired by channel reciprocity in a channel feature-based key establishment technique, we propose the concept of reciprocity of RSS trajectory that guarantees that even when the RSSs of two devices are the same, the identical RSS trajectories of two devices can successfully generate the secret key. In addition, to effectively utilize the RSS trajectories, we design a novel quantization scheme by considering the entropy and efficiency of key generation. Furthermore, we analyze the security of this key establishment procedure in an eavesdropped and monitored environment. We also perform an evaluation of 64-, 128-, 192-, and 256-b key generation in indoor/outdoor environment, and the results indicate that the times are 0.22/0.33, 0.61/0.74, 0.95/1.02, and 1.28/1.46 s, respectively. In addition, the ranges of efficiency and entropy are 0.654-0.795 and 0.968-0.993.

In the above paper, the following acknowledgment of financial support was not included, due to a publication error.

The pervasiveness of wearable devices furnished with state-of-the-art sensors has shown the powerful capability in context-aware applications. However, embedded sensors also become targets for adversaries to launch potential side-channel attacks. In this paper, we present a self-adaptive and pretraining-independent pattern attack that infers a graphical password by recovering the victim's hand movement trajectory via motion sensors of a wrist-worn smart device. With the adaptive pattern inference algorithm, the discovered attack can be launched remotely without requiring previous training data from victims or the prior knowledge about the keyboard input settings. Toward the proposed attack, we create a method to detect the sliding behavior that draws a graphical password on the screen. We also propose an inference algorithm to generate password candidates from hand movement trajectories for different keypad input settings. We implement the discovered attack on a smartwatch and conduct experiments to evaluate the impact of this attack. The evaluation results show that for complex graphical patterns, with a single try, the attack can infer the passwords at a success rate as high as 80%, and the success rate can be further boosted to over 90% within five attempts, which reveals the overlooked privacy information threat caused by sensor data leakage.

The covert communication in the physical layer and WiFi network is an important tendency for the current research on covert channel. On the other hand, the MIMO beamforming technique used in the physical layer of WiFi networks provides great potential for developing covert transmission scheme. To fill this gap, this paper presents a novel covert channel based on the coordinated operations in the control channel and data channel of MIMO system, called CovertMIMO. Under this scheme, the covert transmitter can make some slight modification on the normal uplink process, such that the recovered physical layer signal at the receiver side deviates from the pre-agreed overt signal. Through the deviation, some covert information can be encoded and delivered. To implementing CovertMIMO, this paper considers two kinds of wardens that follow the minimum principle and distribution principle respectively. Against them, the parameter identification is transformed into solving an optimization problem or nonlinear equation set. The transmission capacity and undetectability of CovertMIMO are also analyzed in detail. At last, the effectiveness of CovertMIMO is validated through extensive experiments.

The power flow model for DC power grids has been used theoretically to launch false data injection attacks (FDIAs) against state estimation. We recognize FDIAs are just one possible attack using the power flow model and that the grid topology information within the model implies its discovery may also facilitate topology-based attacks. We show attackers can derive the power flow model, and thus the topology also. Indeed, with incomplete data, attackers can accurately reconstruct regions of the model, or topology, all that is necessary to launch an attack. We also illustrate how to cause such attackers to derive instead a convincing fake model by camouflaging the real model. Consequently, no sensitive information will leak, so attacks based on this fake model will be ineffective, rather alerting grid administrators to the attacker's efforts. Using five test cases included in the MATLAB power flow analysis tool MATPOWER, ranging from 9 to 300 buses, an average 67.0% of the topology may be derived with a 69.1% model accuracy. Lastly, we find reconstructions of small portions of the model sufficient for performing FDIAs with 75% success, and that camouflage prevents 93% of them in all but the 9-bus case.

Traditional signal processing design (e.g., frequency offset and channel estimation) at a receiver treats each packet arrival as an independent process to facilitate decoding and interpreting packet data. In this paper, we enhance the performance of this process in the wireless network domain. We propose STAteful inter-Packet signaL procEssing (STAPLE), a framework of stateful signal processing residing between the physical and link layers. STAPLE transforms the signal processing procedure into a lightweight stateful process that caches in a small-sized memory table physical and link layer header fields as packet state information. The similarity of such information among packets serves as prior knowledge to further enhance the reliability of signal processing and thus improve the wireless network performance. We implement STAPLE on USRP X300-series devices with adapted configurations for 802.11a/b/g/n/ac and 802.15.4. The STAPLE prototype is of low processing complexity and does not change any wireless standard specification. Comprehensive experimental results show that the benefit from STAPLE is universal in various wireless networks.

As a key feature of the next generation wireless networks (5G), heterogeneous networks (HetNets) architecture is embraced as the fundamental network technology to meet the immensely diverse service requirements and characteristics of various devices. By introducing underlaying device-to-device (D2D) communication into HetNets, it is possible to offload the unevenly distributed load from the overloaded macrocell to the uncongested femtocell in multitier HetNets. However, the performance of load balancing (LB) scheme heavily depends on the D2D relay selection method as well as the potential resource reuse interference brought by underlay D2D relaying. In order to improve resources utilization ratio and mitigate the resource reuse interference, an LB strategy based on D2D relaying is proposed. To accommodate more new users into the already overloaded macrocell, the macro user equipment with poor link quality will be first transferred into nearby uncongested femtocells by D2D relaying. Then, the released macrocell resource can be allocated to new users who cannot access femtocell due to location restriction. Furthermore, we also propose a two-stage relay selection and resource allocation scheme which not only minimizes the potential interference caused by resource reuse but also guarantees the transmission requirement of different users. Extensive simulation results demonstrate that by taking advantage of D2D relaying, the proposed LB algorithm can effectively adjust the unbalanced load between macrocell and femtocells, which achieves improved performance of the whole HetNets.

In wireless networks, location distinction aims to detect location changes or facilitate authentication of wireless users. To achieve location distinction, recent research has focused on investigating the spatial uncorrelation property of wireless channels. Specifically, differences in wireless channel characteristics are used to distinguish locations or identify location changes. However, we discover a new attack against all existing location distinction approaches that are built on the spatial uncorrelation property of wireless channels. In such an attack, the adversary can easily hide her location changes or impersonate movements by injecting fake wireless channel characteristics into a target receiver. To defend against this attack, we propose a detection technique that utilizes an auxiliary receiver or antenna to identify these fake channel characteristics. We also discuss such attacks and corresponding defenses in OFDM systems. Experimental results on our USRP-based prototype show that the discovered attack can craft any desired channel characteristic with a successful probability of 95.0 percent to defeat spatial uncorrelation based location distinction schemes and our novel detection method achieves a detection rate higher than 91.2 percent while maintaining a very low false alarm rate.

In this study, we present WindTalker, a novel and practical keystroke inference framework that allows an attacker to infer the sensitive keystrokes on a mobile device through WiFi-based side-channel information. WindTalker is motivated from the observation that keystrokes on mobile devices will lead to different hand coverage and the finger motions, which will introduce a unique interference to the multi-path signals and can be reflected by the channel state information (CSI). The adversary can exploit the strong correlation between the CSI fluctuation and the keystrokes to infer the user's number input. WindTalker presents a novel approach to collect the target's CSI data by deploying a public WiFi hotspot. Compared with the previous keystroke inference approach, WindTalker neither deploys external devices close to the target device nor compromises the target device. Instead, it utilizes the public WiFi to collect user's CSI data, which is easy-to-deploy and difficult-to-detect. In addition, it jointly analyzes the traffic and the CSI to launch the keystroke inference only for the sensitive period where password entering occurs. WindTalker can be launched without the requirement of visually seeing the smart phone user's input process, backside motion, or installing any malware on the tablet. We implemented Windtalker on several mobile phones and performed a detailed case study to evaluate the practicality of the password inference towards Alipay, the largest mobile payment platform in the world. The evaluation results show that the attacker can recover the key with a high successful rate.

Motor vehicles are widely used, quite valuable, and often targeted for theft. Preventive measures include car alarms, proximity control, and physical locks, which can be bypassed if the car is left unlocked, or if the thief obtains the keys. Reactive strategies like cameras, motion detectors, human patrolling, and GPS tracking can monitor a vehicle, but may not detect car thefts in a timely manner. We propose a fast automatic driver recognition system that identifies unauthorized drivers while overcoming the drawbacks of previous approaches. We factor drivers' trips into elemental driving events, from which we extract their driving preference features that cannot be exactly reproduced by a thief driving away in the stolen car. We performed real world evaluation using the driving data collected from 31 volunteers. Experiment results show we can distinguish the current driver as the owner with 97% accuracy, while preventing impersonation 91% of the time.

The emerging paradigm for dynamic spectrum sharing is based on allowing secondary users (SUs) to exploit white space frequency that is not occupied by primary users. White space database provides an opportunity for SUs to obtain spectrum availability information by submitting a location-based query. However, this new paradigm can also be exploited by the attackers to significantly enhance their jamming capability due to the available channel information from spectrum queries, which is expected to increasingly block SUs. The challenge is that the unique characteristics (e.g., lack of the wide range frequencies or continuous broadband) make existing anti-jamming techniques (e.g., direct-sequence spread spectrum and frequency hopping spread spectrum) difficult to be applied. In this paper, we present a novel Jammer Inference-based Jamming Defense (jDefender) framework. The main idea of jDefender is inferring the likelihood of a user being a jammer based on the observed jamming events and then utilizing the inferred attack likelihood to enhance the effectiveness of a series of the proposed anti-jamming strategies. Specifically, we first propose the Channel Allocation-based Jammer Inference scheme to infer the likelihood of an SU being a jammer based on the channels occupied by SUs even under the collusion attack performed by multiple jammers. The strength of the anti-jamming strategies (e.g., puzzle difficulties, available spectrum resources) will be correlated with the possibility of an SU being a jammer to achieve the tradeoff between system performance and jamming tolerance. We then implement the proposed scheme on Universal Software Radio Peripheral and PC. Extensive evaluations are performed to validate the effectiveness of the attacks and countermeasures.

A reactive jammer jams wireless channels only when target devices are transmitting; Compared to constant jamming, reactive jamming is harder to track and compensate against [2], [38]. Frequency hopping spread spectrum (FHSS) and direct sequence spread spectrum (DSSS) have been widely used as countermeasures against jamming attacks. However, both will fail if the jammer jams all frequency channels or has high transmit power. In this paper, we propose an anti-jamming communication system that allows communication in the presence of a broadband and high power reactive jammer. The proposed system transmits messages by harnessing the reaction time of a reactive jammer. It does not assume a reactive jammer with limited spectrum coverage and transmit power, and thus can be used in scenarios where traditional approaches fail. We develop a prototype of the proposed system using GNURadio. Our experimental evaluation shows that when a powerful reactive jammer is present, the prototype still keeps communication, whereas other schemes such as 802.11 DSSS fail completely.

Wireless link signature is a physical layer authentication mechanism, using the multipath effect between a transmitter and a receiver to provide authentication of wireless signals. This paper identifies a new attack, called mimicry attack, against the existing wireless link signature schemes. An attacker can forge a legitimate transmitter's link signature as long as it knows the legitimate signal at the receiver's location, and the attacker does not have to be at exactly the same location as the legitimate transmitter. We also extend the mimicry attack to multiple-input multiple-output (MIMO) systems, and conclude that the mimicry attack is feasible only when the number of attacker' antennas is equal to or larger than that of the receiver's antennas. To defend against the mimicry attack, this paper proposes a novel construction for wireless link signature, called time-synched link signature, by integrating cryptographic protection and time factor into wireless physical layer features. Experimental results confirm that the mimicry attack is a real threat and the newly proposed time-synched link signatures are effective in physical layer authentication.

We propose a novel wireless technique named pinpoint waveforming to achieve the location-restricted service access control, i.e., providing wireless services to users at eligible locations only. The proposed system is inspired by the fact that when two identical wireless signals arrive at a receiver simultaneously, they will constructively interfere with each other to form a boosted signal whose amplitude is twice of that of an individual signal. As such, the location-restricted service access control can be achieved through transmitting at a weak power, so that receivers at undesired locations (where the constructive interference vanishes), will experience a low signal-to-noise ratio (SNR), and hence a high bit error rate that retards the correct decoding of received messages. At the desired location (where the constructive interference happens), the receiver obtains a boosted SNR that enables the correct message decoding. To solve the difficulty of determining an appropriate transmit power, we propose to entangle the original transmit signals with jamming signals of opposite phase. The jamming signals can significantly reduce the SNR at the undesired receivers but cancel each other at the desired receiver to cause no impact. With the jamming entanglement, the transmit power can be any value specified by the system administrator. To enable the jamming entanglement, we create the channel calibration technique that allows the synchronization of transmit signals at the desired location. We develop a prototype system using the Universal Software Defined Radio Peripherals (USRPs). The evaluation results show that the receiver at the desired location obtains a throughput ranging between 0.9 and 0.93, whereas an eavesdropper that is 0.3 meter away from a desired location has a throughput approximately equal to 0.

CSI fingerprint localization is an advanced and promising technique for indoor localization, which identifies the user's location by mapping his measured CSI against the server's CSI fingerprint database. This approach is highlighted due to its high granularity for location distinction and strong robustness to noise disturbances, but it also causes potential privacy leakage for the three participants in localization process: the user, the server, and the AP. Currently, there has been little research done on this issue, and the existing work often ignores the privacy concern on the AP. To fill the gap, this paper develops a privacypreserving fuzzy localization scheme with CSI fingerprint. On one hand, it leverages the property of CSI training to guarantee the randomness and independence of the user's measurement in each time of localization, and uses homomorphic encryption to achieve the data transmission and measurement comparison in cipher. These operations enable our scheme to preserve the location privacy of the user and APs as well as the data privacy of the server. On the other hand, the adoption of CSI fingerprint and fuzzy logic enhances the localization accuracy greatly. Through simulation experiments performed on CRAWDAD database, the efficiency of our proposed scheme is validated.

In wireless networks, location distinction aims to detect location changes or facilitate authentication of wireless users. To achieve location distinction, recent research has been focused on investigating the spatial uncorrelation property of wireless channels. Specifically, the differences of wireless channel characteristics are used to distinguish locations or identify location changes.

Cognitive Radio Network (CRN) is regarded as a promising way to address the increasing demand for wireless channel resources. It solves the channel resource shortage problem by allowing a Secondary User (SU) to access the channel of a Primary User (PU) when the channel is not occupied by the PU. The latest FCC's rule in May 2012 enforces database-driven CRNs, in which an SU queries a database to obtain spectrum availability information by submitting a location based query. However, one concern about database-driven CRNs is that the queries sent by SUs will inevitably leak the location information. In this study, we identify a new kind of attack against location privacy of database-drive CRNs. Instead of directly learning the SUs' locations from their queries, our discovered attacks can infer an SU's location through his used channels. We propose Spectrum Utilization based Location Inferring Algorithm that enables the attacker to geo-locate an SU. To thwart location privacy leaking from query process, we propose a novel Private Spectrum Availability Information Retrieval scheme that utilizes a blind factor to hide the location of the SU. To defend against the discovered attack, we propose a novel prediction based Private Channel Utilization protocol that reduces the possibilities of location privacy leaking by choosing the most stable channels. We implement our discovered attack and proposed scheme on the data extracted from Google Earth Coverage Maps released by FCC. Experiment results show that the proposed protocols can significantly improve the location privacy.

The Database-driven Cognitive Radio Network is regarded as a promising way for a better utilization of radio channels without introducing the interference to the primary user. However, it is also facing a series of security threats. In this study, we identify a new kind of location privacy related attack which could geo-locate a secondary user from the spectrum he used. We propose a Spectrum Utilization based Location Inference Algorithm, which is based on the intersection of the possible location sets revealed by each channel access or channel transition event under the presence of the primary user. We implement our algorithm on the data extracted from Google Earth Coverage Maps released by FCC. Our experiement results show that, $80\%$ SUs could be located to 10 cells based on 25 or less channels.

Reactive jamming is not only cost effective, but also hard to track and remove due to its intermittent jamming behaviors. Frequency Hopping Spread Spectrum (FHSS) and Direct Sequence Spread Spectrum (DSSS) have been widely used to defend against jamming attacks. However, both will fail if the jammer jams all frequency channels or has high transmit power. In this paper, we propose BitTrickle, an anti-jamming wireless communication scheme that allows communication in the presence of a broadband and high power reactive jammer by exploiting the reaction time of the jammer. We develop a prototype of BitTrickle using the USRP platform running GNURadio. Our evaluation shows that when under powerful reactive jamming, BitTrickle still maintains communication, whereas other schemes such as 802.11 DSSS fail completely.

Wireless link signature is a physical layer authentication mechanism, which uses the unique wireless channel characteristics between a transmitter and a receiver to provide authentication of wireless channels. A vulnerability of existing link signature schemes has been identified by introducing a new attack, called mimicry attack. To defend against the mimicry attack, we propose a novel construction for wireless link signature, called time-synched link signature, by integrating cryptographic protection and time factor into traditional wireless link signatures. We also evaluate the mimicry attacks and the time-synched link signature scheme on the USRP2 platform running GNURadio. The experimental results demonstrate the effectiveness of time-synched link signature.

Enterprise networks today host a wide variety of network services, which often depend on each other to provide and support network-based services and applications. Understanding such dependencies is essential for maintaining the well-being of an enterprise network and its applications, particularly in the presence of network attacks and failures. In a typical enterprise network, which is complex and dynamic in configuration, it is non-trivial to identify all these services and their dependencies. Several techniques have been developed to learn such dependencies automatically. However, they are either too complex to fine tune or cluttered with false positives and/or false negatives. In this paper, we propose a suite of novel techniques and develop a new tool named NSDMiner (which stands for Mining for Network Service Dependencies) to automatically discover the dependencies between network services from passively collected network traffic. NSDMiner is non-intrusive; it does not require any modification of existing software, or injection of network packets. More importantly, NSDMiner achieves higher accuracy than previous network-based approaches. Our experimental evaluation, which uses network traffic collected from our campus network, shows that NSDMiner outperforms the two best existing solutions significantly.

A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including interacting bad measurements introduced by arbitrary, nonrandom causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers. In this article, we expose an unknown vulnerability of existing bad measurement detection algorithms by presenting and analyzing a new class of attacks, called false data injection attacks , against state estimation in electric power grids. Under the assumption that the attacker can access the current power system configuration information and manipulate the measurements of meters at physically protected locations such as substations, such attacks can introduce arbitrary errors into certain state variables without being detected by existing algorithms. Moreover, we look at two scenarios, where the attacker is either constrained to specific meters or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios to change the results of state estimation in arbitrary ways. We also extend these attacks to generalized false data injection attacks , which can further increase the impact by exploiting measurement errors typically tolerated in state estimation. We demonstrate the success of these attacks through simulation using IEEE test systems, and also discuss the practicality of these attacks and the real-world constraints that limit their effectiveness.

Spread spectrum techniques such as Direct Sequence Spread Spectrum (DSSS) and Frequency Hopping (FH) have been commonly used for anti-jamming wireless communication. However, traditional spread spectrum techniques require that sender and receivers share a common secret in order to agree upon, for example, a common hopping sequence (in FH) or a common spreading code sequence (in DSSS). Such a requirement prevents these techniques from being effective for anti-jamming broadcast communication, where a jammer may learn the key from a compromised receiver and then disrupt the wireless communication. In this paper, we develop a novel Delayed Seed-Disclosure DSSS (DSD-DSSS) scheme for efficient anti-jamming broadcast communication. DSD-DSSS achieves its anti-jamming capability through randomly generating the spreading code sequence for each message using a random seed and delaying the disclosure of the seed at the end of the message. We also develop an effective protection mechanism for seed disclosure using content-based code subset selection. DSD-DSSS is superior to all previous attempts for anti-jamming spread spectrum broadcast communication without shared keys. In particular, even if a jammer possesses real-time online analysis capability to launch reactive jamming attacks, DSD-DSSS can still defeat the jamming attacks with a very high probability. We evaluate DSD-DSSS through both theoretical analysis and a prototype implementation based on GNU Radio; our evaluation results demonstrate that DSD-DSSS is practical and have superior security properties.

Jamming resistance is crucial for applications where reliable wireless communication is required. Spread spectrum techniques such as Frequency Hopping Spread Spectrum (FHSS) and Direct Sequence Spread Spectrum (DSSS) have been used as countermeasures against jamming attacks. Traditional anti-jamming techniques require that senders and receivers share a secret key in order to communicate with each other. However, such a requirement prevents these techniques from being effective for anti-jamming broadcast communication, where a jammer may learn the shared key from a compromised or malicious receiver and disrupt the reception at normal receivers. In this paper, we propose a Randomized Differential DSSS (RD-DSSS) scheme to achieve anti-jamming broadcast communication without shared keys. RD-DSSS encodes each bit of data using the correlation of unpredictable spreading codes. Specifically, bit ``0'' is encoded using two different spreading codes, which have low correlation with each other, while bit ``1'' is encoded using two identical spreading codes, which have high correlation. To defeat reactive jamming attacks, RD-DSSS uses multiple spreading code sequences to spread each message and rearranges the spread output before transmitting it. Our theoretical analysis and simulation results show that RD-DSSS can effectively defeat jamming attacks for anti-jamming broadcast communication without shared keys.

Spread spectrum techniques (e.g., Frequency Hopping (FH), Direct Sequence Spread Spectrum (DSSS)) have been widely used for anti-jamming wireless communications. Such techniques require that communicating devices agree on a shared secret before communication. However, it is non-trivial for two devices that do not share any secret to establish one in presence of a jammer. Recently, several schemes relying on Uncoordinated Frequency Hopping (UFH) were proposed to allow two devices to establish a secret key using Diffie-Hellman (DH) key establishment protocol in presence of jammers. Unfortunately, all these schemes are limited in efficiency. In this paper, we propose a novel scheme named USD-FH, which uses Uncoordinated Seed Disclosure in Frequency Hopping to establish a shared secret in presence of jammers. The basic idea is to transmit each DH key establishment message using a one-time pseudo-random hopping pattern and disclose the corresponding seed in an uncoordinated manner before the actual message. Due to the large number of channels available for wireless communication, the jammers cannot control all channels at the same time. When the receiver and the sender use the same channel during seed disclosure, the receiver can get the seed. If the jammer does not listen on the same channel (and thus it does not know the hopping pattern), the receiver can receive the actual message without being jammed. We validate USD-FH through both theoretical analysis and simulation. Our results show that USD-FH is much more efficient and robust than previous solutions.

To address the increasing demand for wireless bandwidth, cognitive radio networks (CRNs) have been proposed to increase the efficiency of channel utilization; they enable the sharing of channels among secondary (unlicensed) and primary (licensed) users on a non-interference basis. A secondary user in a CRN should constantly monitor for the presence of a primary user's signal to avoid interfering with the primary user. However, to gain unfair share of radio channels, an attacker (e.g., a selfish secondary user) may mimic a primary user's signal to evict other secondary users. Therefore, a secure primary user detection method that can distinguish a primary user's signal from an attacker's signal is needed. A unique challenge in addressing this problem is that Federal Communications Commission (FCC) prohibits any modification to primary users. Consequently, existing cryptographic techniques cannot be used directly. In this paper, we develop a novel approach for authenticating primary users' signals in CRNs, which conforms to FCC's requirement. Our approach integrates cryptographic signatures and wireless link signatures (derived from physical radio channel characteristics) to enable primary user detection in the presence of attackers. Essential to our approach is a {\em helper node} placed physically close to a primary user. The helper node serves as a "bridge" to enable a secondary user to verify cryptographic signatures carried by the helper node's signals and then obtain the helper node's authentic link signatures to verify the primary user's signals. A key contribution in our paper is a novel physical layer authentication technique that enables the helper node to authenticate signals from its associated primary user. Unlike previous techniques for link signatures, our approach explores the geographical proximity of the helper node to the primary user, and thus does not require any training process.

A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including the interacting bad measurements introduced by arbitrary, non-random causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.

The paper deals with the problem of the carrier frequency-offset (CFO) estimation for wireless communications. Since the well-known maximum-likelihood (ML) CFO estimator is rather complex, many complexity-reduced CFO estimators have so far been proposed. However, most of them are based on periodic training sequences and have limited estimation range. We propose a general framework called composite frequency- offset estimator (CFE), which can extend the estimation range of any range-limited correlation-based CFO estimator up to the full transmission spectrum. We first formulate the CFO estimation as a problem of composite hypothesis testing, and then solve the problem by the composite hypothesis testing approaches. We show that the composite frequency-offset estimate is the ML estimate as long as the original range-limited estimator is unbiased and attains the Cramer-Rao Lower Bound. The CFE also allows a flexible tradeoff between the estimation range and computational complexity. Finally, numerical results show the efficiency of the proposed method.