7 papers
Beyond Best Response: Quantal Stackelberg Deception as Insurance Against Attacker Misspecification
Asif Rahman, Md. Abu Sayed, Ahmed Ann Noor Ryen +3
Stackelberg Security Games (SSG) assume that an attacker observes the defender's strategy and chooses the target that maximizes their expected utility perfectly. In most realistic…
Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learning
Zelin Wan, Jin-Hee Cho, Mu Zhu +3
Unmanned Aerial Vehicles (UAVs) are valuable for mission-critical systems like surveillance, rescue, or delivery. Not surprisingly, such systems attract cyberattacks, including Den…
Coordinated Multi-Domain Deception: A Stackelberg Game Approach
Md Abu Sayed, Asif Rahman, Ahmed Hemida +2
This paper explores coordinated deception strategies by synchronizing defenses across coupled cyber and physical systems to mislead attackers and strengthen defense mechanisms. We…
Planning Stealthy Backdoor Attacks in MDPs with Observation-Based Triggers
Xinyi Wei, Shuo Han, Ahmed H. Hemida +2
This paper investigates backdoor attack planning in stochastic control systems modeled as Markov Decision Processes (MDPs). A backdoor attack involves an adversary deploying a poli…
Active Inference via Incentive Design: Eliciting Divergent Behaviors under Partial Observability
Xinyi Wei, Chongyang Shi, Shuo Han +3
We introduce Active Inference through Incentive Design, a framework in which a leader offers strategic incentive to elicit diverging behaviors from followers of different types, th…
Reactive Synthesis of Sensor Revealing Strategies in Hypergames on Graphs
Sumukha Udupa, Ahmed Hemida, Charles A. Kamhoua +1
In many security applications of cyber-physical systems, a system designer must guarantee that critical missions are satisfied against attacks in the sensors and actuators of the C…
Decision Theory-Guided Deep Reinforcement Learning for Fast Learning
Zelin Wan, Jin-Hee Cho, Mu Zhu +3
This paper introduces a novel approach, Decision Theory-guided Deep Reinforcement Learning (DT-guided DRL), to address the inherent cold start problem in DRL. By integrating decisi…