2017/09/13 by Vahid Behzadan, Behzadan, Vahid, Arslan Munir +1
Computer Science · Decision Sciences · Engineering · #Computer Science and Game Theory (cs.GT) #Cryptography and Security (cs.CR) #Distributed #FOS: Computer and information sciences #FOS: Electrical engineering #Network Security and Intrusion Detection #Parallel #Simulation Techniques and Applications #Smart Grid Security and Resilience #Systems and Control (eess.SY) #and Cluster Computing (cs.DC) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1709.04137
openalex publication_date 2017/09/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 approaches to the modeling of CAS as dynamical, data-driven, and game-theoretic systems, and develop quantitative definitions of attack, vulnerability, and resilience in the context of CAS security. Furthermore, we propose a comprehensive set of schemes for classification of attacks and attack surfaces in CAS, complemented with examples of practical attacks. Building on this foundation, we propose a framework based on reinforcement learning for simulation and analysis of attacks on CAS, and demonstrate its performance through three real-world case studies of targeting power grids, destabilization of terrorist organizations, and manipulation of machine learning agents. We also discuss potential mitigation techniques, and remark on future research directions in analysis and design of secure complex adaptive systems.