As cyber threats rapidly evolve, autonomous cyber deception emerges as a promising approach to enhance deterrence and resilience. By leveraging AI and machine learning techniques, autonomous deception systems can dynamically adapt to adversary behavior, creating a proactive and personalized defense strategy.
Autonomous cyber deception employs a multi-layered architecture, integrating honeypots, decoys, and deceptive signals to mislead and deter adversaries. These systems continuously learn from attacker interactions, using reinforcement learning to optimize the placement and configuration of deceptive elements (Bilinski et al., 2021). By presenting a dynamic and adaptive attack surface, autonomous deception increases the cost and complexity of attacks, deterring adversaries and buying time for defenders to respond (Fugate & Ferguson-Walter, 2019).
Cognitive models play a crucial role in informing autonomous deception strategies. By modeling and predicting attacker decision-making processes, these models enable the creation of personalized and psychologically persuasive deceptive signals (Cranford et al., 2020). Leveraging insights from human cognition, autonomous deception systems can exploit cognitive biases and manipulate attacker beliefs, leading to increased compliance with deceptive signals and improved overall defense (Aggarwal et al., 2019).
Autonomous cyber deception enhances resilience by providing a proactive and adaptive defense mechanism. By continuously learning and adapting to attacker behavior, these systems can maintain effectiveness even as adversaries evolve their tactics and techniques (Fugate & Ferguson-Walter, 2019). Furthermore, the integration of deception into intrusion detection and response systems enables rapid containment and mitigation of threats, minimizing the impact of successful breaches (Bilinski et al., 2021).
Autonomous cyber deception represents a paradigm shift in cybersecurity, leveraging AI and cognitive models to create a proactive, adaptive, and psychologically informed defense strategy. By deterring adversaries, buying time for defenders, and enhancing resilience, autonomous deception systems hold great promise in the face of ever-evolving cyber threats.
References:
Aggarwal, P., Gonzalez, C., & Dutt, V. (2019). Cyber-security: Role of deception in cyber-attack detection. Advances in Human Factors in Cybersecurity, 85-96.
Bilinski, M., Ferguson-Walter, K., Fugate, S., Mauger, R., & Watson, K. (2021). You only lie twice: A multi-round cyber deception game of questionable veracity. Topics in Cognitive Science, 13(2), 410-431.
Cranford, E. A., Gonzalez, C., Aggarwal, P., Cooney, S., Tambe, M., & Lebiere, C. (2020). Towards personalized deceptive signaling for cyber defense using cognitive models. In Proceedings of the 17th Annual Meeting of the ICCM.
Fugate, S., & Ferguson-Walter, K. (2019). Artificial intelligence and game theory models for defending critical networks with cyber deception. AI Magazine, 40(1), 49-62.