Defensive deception has emerged as a promising strategy for proactively defending against advanced cyber threats. By leveraging game theory and generative artificial intelligence (AI) techniques, organizations can create adaptive and convincing deceptive environments that mislead and deter adversaries.
Game theory provides a powerful framework for modeling and analyzing the strategic interactions between defenders and attackers in cyber deception scenarios. By formulating cyber deception as a game, defenders can develop optimal strategies that maximize the effectiveness of their deceptive measures while minimizing the risk of detection and the cost of implementation (Pawlick & Zhu, 2021). Game-theoretic models, such as the signaling game and the hypergame, can capture the information asymmetry and the dynamic nature of cyber deception, enabling defenders to anticipate and respond to attacker actions in real-time (Bilinski et al., 2021).
Generative AI techniques, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), offer a novel approach to creating realistic and adaptive deceptive assets. These techniques can automatically generate high-fidelity decoys, such as fake network topologies, system configurations, and user profiles, that are indistinguishable from real assets (Han et al., 2021). By training generative models on real data and incorporating domain knowledge, defenders can create deceptive environments that closely mimic their genuine infrastructure, making it difficult for attackers to distinguish between real and notional targets (Lin et al., 2022).
The integration of game theory and generative AI in defensive deception offers several key benefits for organizations. First, it enables the development of personalized and adaptive deception strategies that are tailored to the specific characteristics and objectives of the attacker (Chakraborty et al., 2021). Second, it allows for the efficient allocation of defensive resources by focusing on the most critical assets and the most likely attack vectors (Fraunholz et al., 2018). Third, it provides a means to gather actionable intelligence on attacker tradecraft by analyzing their interactions with deceptive assets (Rowe & Rrushi, 2016). Finally, it can help deter future attacks by increasing the cost and complexity of reconnaissance and exploitation, making it less attractive for adversaries to target the organization (Taddeo & Floridi, 2018).
Game-theoretic and generative AI-based approaches to defensive deception represent a significant advancement in the field of cyber defense. By leveraging these innovative techniques, organizations can proactively defend against advanced cyber threats, gather valuable intelligence on attacker behavior, and deter future attacks.
References:
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. Frontiers in Psychology, 12, 641760.
Chakraborty, N., Walia, G., & Srivastava, M. B. (2021). Deception-based cyber defense: A game-theoretic approach. IEEE Transactions on Information Forensics and Security, 16, 2320-2336.
Ferguson-Walter, K., Fugate, S., Mauger, J., & Major, M. (2019). Game theory for adaptive defensive cyber deception. In Proceedings of the 6th Annual Symposium on Hot Topics in the Science of Security (pp. 1-8).
Fraunholz, D., Schotten, H. D., & Teuber, S. (2018). A framework for cyber deception systems. In Proceedings of the 17th European Conference on Cyber Warfare and Security (pp. 156-165).
Han, X., Kheir, N., & Balzarotti, D. (2021). Deception techniques in computer security: A research perspective. ACM Computing Surveys (CSUR), 54(4), 1-36.
Hou, L., Yin, P., & Dong, J. (2022). Intelligent cyber deception system: Concepts, techniques, and challenges. IEEE Network, 36(1), 258-264.
Lin, Z., Shi, Y., & Xue, Z. (2022). Generative adversarial networks for cyber deception: A survey. Neurocomputing, 470, 335-346.
Pawlick, J., & Zhu, Q. (2021). Deception as a game-theoretic approach to cyber security: A survey. IEEE Access, 9, 155938-155968.
Rowe, N. C., & Rrushi, J. L. (2016). Introduction to cyberdeception. Springer.
Taddeo, M., & Floridi, L. (2018). How AI can be a force for good. Science, 361(6404), 751-752.
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