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1. A Safety Constrained Control Framework for UAVs in GPS Denied Environment

   https://doi.org/10.1109/CDC42340.2020.9304304  

   Wan, Wenbin; Kim, Hunmin; Hovakimyan, Naira; Sha, Lui; Voulgaris, Petros G. (December 2020, 59th IEEE Conference on Decision and Control)

   null (Ed.)

   Unmanned aerial vehicles (UAVs) suffer from sensor drifts in GPS denied environments, which can lead to potentially dangerous situations. To avoid intolerable sensor drifts in the presence of GPS spoofing attacks, we propose a safety constrained control framework that adapts the UAV at a path re-planning level to support resilient state estimation against GPS spoofing attacks. The attack detector is used to detect GPS spoofing attacks and provides a switching criterion between the robust control mode and emergency control mode. An attacker location tracker (ALT) is developed to track the attacker's location and estimate the spoofing device's output power by the unscented Kalman filter (UKF) with sliding window outputs. Using the estimates from ALT, we design an escape controller (ESC) based on the model predictive controller (MPC) such that the UAV escapes from the effective range of the spoofing device within the escape time. 

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2. Backdoor Attacks and Defenses on Semantic-Symbol Reconstruction in Semantic Communications

   Zhou, Y; Hu, RQ; Qian, Y (December 2024, IEEE Globecom 2024)

   Semantic communication is of crucial importance for the next-generation wireless communication networks. The existing works have developed semantic communication frameworks based on deep learning. However, systems powered by deep learning are vulnerable to threats such as backdoor attacks and adversarial attacks. This paper delves into backdoor attacks targeting deep learning-enabled semantic communication systems. Since current works on backdoor attacks are not tailored for semantic communication scenarios, a new backdoor attack paradigm on semantic symbols (BASS) is introduced, based on which the corresponding defense measures are designed. Specifically, a training framework is proposed to prevent BASS. Additionally, reverse engineering-based and pruning-based defense strategies are designed to protect against backdoor attacks in semantic communication. Simulation results demonstrate the effectiveness of both the proposed attack paradigm and the defense strategies. 

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3. SoundFence: Securing Ultrasonic Sensors in Vehicles Using Physical-Layer Defense

   https://doi.org/10.1109/SECON52354.2021.9491590  

   Lou, Jianzhi; Yan, Qiben; Hui, Qing; Zeng, Huacheng (July 2021, 2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON))

   null (Ed.)

   Autonomous vehicles (AVs), equipped with numerous sensors such as camera, LiDAR, radar, and ultrasonic sensor, are revolutionizing the transportation industry. These sensors are expected to sense reliable information from a physical environment, facilitating the critical decision-making process of the AVs. Ultrasonic sensors, which detect obstacles in a short distance, play an important role in assisted parking and blind spot detection events. However, due to their weak security level, ultrasonic sensors are particularly vulnerable to signal injection attacks, when the attackers inject malicious acoustic signals to create fake obstacles and intentionally mislead the vehicles to make wrong decisions with disastrous aftermath. In this paper, we systematically analyze the attack model of signal injection attacks toward moving vehicles. By considering the potential threats, we propose SoundFence, a physical-layer defense system which leverages the sensors’ signal processing capability without requiring any additional equipment. SoundFence verifies the benign measurement results and detects signal injection attacks by analyzing sensor readings and the physical-layer signatures of ultrasonic signals. Our experiment with commercial sensors shows that SoundFence detects most (more than 95%) of the abnormal sensor readings with very few false alarms, and it can also accurately distinguish the real echo from injected signals to identify injection attacks. 

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4. Drift with Devil: Security of Multi-Sensor Fusion based Localization in High-Level Autonomous Driving under GPS Spoofing

   Shen, Junjie; Won, Jun Yeon; Chen, Zeyuan; Chen, Qi Alfred (January 2020, Usenix Security Symposium)

   null (Ed.)

   For high-level Autonomous Vehicles (AV), localization is highly security and safety critical. One direct threat to it is GPS spoofing, but fortunately, AV systems today predominantly use Multi-Sensor Fusion (MSF) algorithms that are generally believed to have the potential to practically defeat GPS spoofing. However, no prior work has studied whether today’s MSF algorithms are indeed sufficiently secure under GPS spoofing, especially in AV settings. In this work, we perform the first study to fill this critical gap. As the first study, we focus on a production-grade MSF with both design and implementation level representativeness, and identify two AV-specific attack goals, off-road and wrong-way attacks. To systematically understand the security property, we first analyze the upper-bound attack effectiveness, and discover a take-over effect that can fundamentally defeat the MSF design principle. We perform a cause analysis and find that such vulnerability only appears dynamically and non-deterministically. Leveraging this insight, we design FusionRipper, a novel and general attack that opportunistically captures and exploits take-over vulnerabilities. We evaluate it on 6 real-world sensor traces, and find that FusionRipper can achieve at least 97% and 91.3% success rates in all traces for off-road and wrongway attacks respectively. We also find that it is highly robust to practical factors such as spoofing inaccuracies. To improve the practicality, we further design an offline method that can effectively identify attack parameters with over 80% average success rates for both attack goals, with the cost of at most half a day. We also discuss promising defense directions. 

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5. Defense against Black Hole Attacks in Wireless Sensor Network with Anomaly Report Cycling

   Vieira, Marcel; Liu, Hong (May 2024, 2024 International Wireless Communications and Mobile Computing (IWCMC))

   Wireless Sensor Network (WSN) becomes the dominate last-mile connection to cyber-physical systems and Internet-of-Things. However, WSN opens new attack surfaces such as black holes, where sensing information gets lost during relay towards base stations. Current defense mechanisms against black hole attacks require substantial energy consumption, reducing the system's lifetime. This paper proposes a novel approach to detect and recover from black hole attacks using an improved version of Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. LEACH is an energy-efficient routing protocol for groups of battery-operated sensor nodes in hierarchy. A round of selection for cluster heads is scheduled in a set time. We propose to improve LEACH with Anomaly Report Cycling (ARC-LEACH), tradeoff between security strength and energy cost. ARC-LEACH absorbs an attack when it occurs by rotating cluster heads to reestablish communication and then sending a message from the base station to coordinate all nodes against the malicious nodes. ARC-LEACH actively blocks malicious nodes while leveraging the resilience of LEACH for stronger resistance to blackhole attacks. ARC-LEACH can provide more defense capability when under attack from multiple malicious nodes that would otherwise be defenseless by LEACH, with only minor increase in energy consumption. 

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