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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. Counter UAV Swarms: Challenges, Considerations, and Future Directions in UAV Warfare

   https://doi.org/10.1109/MWC.003.2400047  

   Sherman, Michelle; Shao, Sihua; Sun, Xiang; Zheng, Jun (January 2024, IEEE Wireless Communications)

   Modern advances in unmanned aerial vehicle (UAV) technology have widened the scope of commercial and military applications. However, the increased dependency on wireless communications exposes UAVs to potential attacks and introduces new threats, especially from UAVs designed with the malicious intent of targeting vital infrastructures. Significant efforts have been made from researchers and other United States (U.S.) Department of Defense (DoD) agencies for developing countermeasures for detection, interception, or destruction of the malicious UAVs. One promising countermeasure is the use of a counter UAV (CUAV) swarm to detect, track, and neutralize the malicious UAV. This paper aims to recognize the state-of-the-art swarm intelligence algorithms for achieving cooperative capture of a mobile target UAV. The major design and implementation challenges for swarm control, algorithm architecture, and safety protocols are considered. A prime challenge for UAV swarms is a robust communication infrastructure to enable accurate data transfer between UAVs for efficient path planning. A multi-agent deep reinforcement learning approach is applied to train a group of CUAVs to intercept a faster malicious UAV, while avoiding collisions among other CUAVs and non-cooperating obstacles (i.e. other aerial objects maneuvering in the area). The impact of the latency incurred through UAV-to-UAV communications is showcased and discussed with preliminary numerical results. 

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3. Biased Electropermanent Magnetic Docking Design for Neutral Buoyancy UAV Deployment

   https://doi.org/10.2514/6.2024-1694  

   Martin, Jacob; Satme, Joud; Downey, Austin R. (January 2024, AIAA SCITECH 2024 Forum.)

   This paper introduces a novel approach to enhance the docking mechanism of sensor packages deployed on bridges using unmanned aerial vehicles (UAVs). The current electropermanent magnet (EPM) system faces challenges in achieving efficient and stable docking due to factors such as airflow, GPS stabilization, and the time required for EPM activation. To address these issues, a biased EPM design is proposed, utilizing additional permanent magnets to achieve neutral buoyancy during UAV deployment. This design optimally balances the weight of the drone and sensor package, providing advantages such as improved stability against external factors and reduced pilot fatigue. Experimental results demonstrate the feasibility of the proposed design, indicating enhanced hold force and an extended range for efficient docking. 

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4. Explainable AI for Prioritizing and Deploying Defenses for Cyber-Physical System Resiliency

   https://doi.org/10.1109/TPS-ISA58951.2023.00032  

   Ray, Indrajit; Sreedharan, Sarath; Podder, Rakesh; Bashir, Shadaab Kawnain; Ray, Indrakshi (November 2023, IEEE)

   The adoption of digital technology in industrial control systems (ICS) enables improved control over operation, ease of system diagnostics and reduction in cost of maintenance of cyber physical systems (CPS). However, digital systems expose CPS to cyber-attacks. The problem is grave since these cyber-attacks can lead to cascading failures affecting safety in CPS. Unfortunately, the relationship between safety events and cyber-attacks in ICS is ill-understood and how cyber-attacks can lead to cascading failures affecting safety. Consequently, CPS operators are ill-prepared to handle cyber-attacks on their systems. In this work, we envision adopting Explainable AI to assist CPS oper-ators in analyzing how a cyber-attack can trigger safety events in CPS and then interactively determining potential approaches to mitigate those threats. We outline the design of a formal framework, which is based on the notion of transition systems, and the associated toolsets for this purpose. The transition system is represented as an AI Planning problem and adopts the causal formalism of human reasoning to asssit CPS operators in their analyses. We discuss some of the research challenges that need to be addressed to bring this vision to fruition. 

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5. Performance analysis of capsule networks for detecting GPS spoofing attacks on unmanned aerial vehicles

   https://doi.org/10.1007/s10207-024-00978-x  

   Talaei Khoei, Tala; Al Shamaileh, Khair; Devabhaktuni, Vijaya Kumar; Kaabouch, Naima (February 2025, International Journal of Information Security)

   NA (Ed.)

   Unmanned aerial vehicles (UAVs) are prone to several cyber-attacks, including global positioning system (GPS) spoofing. The use of machine learning and deep learning are becoming increasingly common for UAV GPS spoofing attack detection; however, these approaches have some limitations, such as a high rate of false alarm and misdetection. We propose using capsule networks to detect and classify UAV-focused GPS spoofing attacks. This paper compares simple capsule networks, efficient capsule networks, dual attention capsule networks, and convolutional neural network in terms of accuracy, probability of detection, probability of misdetection, probability of false alarm, prediction time, training time per sample, and memory size. The results indicate that the Efficient-capsule network outperforms the other models, as demonstrated by an accuracy of 99.1%, a probability of detection of 99.9%, a probability of misdetection of 0.1%, a probability of false alarm of 0.37%, a prediction time of 0.5 seconds, a training time per sample of 0.2 seconds, and a memory size of 123 mebibytes for binary classification. 

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