401 citations · 529 across the 35 of their papers we have counts for
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Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger
Vahid Behzadan, Arslan Munir
Recent developments have established the vulnerability of deep Reinforcement Learning (RL) to policy manipulation attacks via adversarial perturbations. In this paper, we investiga…
Models and Framework for Adversarial Attacks on Complex Adaptive Systems
Vahid Behzadan, Arslan Munir
We introduce the paradigm of adversarial attacks that target the dynamics of Complex Adaptive Systems (CAS). To facilitate the analysis of such attacks, we present multiple approac…
Cyber-Physical Attacks on UAS Networks- Challenges and Open Research Problems
Vahid Behzadan
Assignment of critical missions to unmanned aerial vehicles (UAV) is bound to widen the grounds for adversarial intentions in the cyber domain, potentially ranging from disruption…
A Game-Theoretic Model for Analysis and Design of Self-Organization Mechanisms in IoT
Vahid Behzadan, Banafsheh Rekabdar
We propose a framework based on Network Formation Game for self-organization in the Internet of Things (IoT), in which heterogeneous and multi-interface nodes are modeled as self-i…
Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks
Vahid Behzadan, Arslan Munir
Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples. In this work, we establish that reinf…