Dynamic Bayesian Games for Adversarial and Defensive Cyber Deception

TL;DR

This paper models cyber deception using dynamic Bayesian games, capturing attacker-defender interactions with asymmetric information and deception techniques, to develop strategic defenses against sophisticated cyber threats.

Contribution

It introduces a game-theoretic framework incorporating Bayesian and signaling games to analyze cyber deception and strategic defense mechanisms.

Findings

Analysis of Nash, Bayesian Nash, and perfect Bayesian Nash equilibria.

Demonstration of defense strategies using honey files and honeypots.

Case study on Advanced Persistent Threats (APTs) and industrial processes.

Abstract

Security challenges accompany the efficiency. The pervasive integration of information and communications technologies (ICTs) makes cyber-physical systems vulnerable to targeted attacks that are deceptive, persistent, adaptive and strategic. Attack instances such as Stuxnet, Dyn, and WannaCry ransomware have shown the insufficiency of off-the-shelf defensive methods including the firewall and intrusion detection systems. Hence, it is essential to design up-to-date security mechanisms that can mitigate the risks despite the successful infiltration and the strategic response of sophisticated attackers. In this chapter, we use game theory to model competitive interactions between defenders and attackers. First, we use the static Bayesian game to capture the stealthy and deceptive characteristics of the attacker. A random variable called the \textit{type} characterizes users' essences and objectives, e.g., a legitimate user or an attacker. The realization of the user's type is private information due to the cyber deception. Then, we extend the one-shot simultaneous interaction into the one-shot interaction with asymmetric information structure, i.e., the signaling game. Finally, we investigate the multi-stage transition under a case study of Advanced Persistent Threats (APTs) and Tennessee Eastman (TE) process. Two-Sided incomplete information is introduced because the defender can adopt defensive deception techniques such as honey files and honeypots to create sufficient amount of uncertainties for the attacker. Throughout this chapter, the analysis of the Nash equilibrium (NE), Bayesian Nash equilibrium (BNE), and perfect Bayesian Nash equilibrium (PBNE) enables the policy prediction of the adversary and the design of proactive and strategic defenses to deter attackers and mitigate losses.