Edward Colbert

Edward Colbert, Alexander Kott, Lawrence Knachel

We demonstrate that game-theoretic calculations serve as a useful tool for assisting cyber wargaming teams in identifying useful strategies. We note a significant similarity between formulating cyber wargaming strategies and the methodology known in military practice as Course of Action (COA) generation. For scenarios in which the attacker must penetrate multiple layers in a defense-in-depth security configuration, an accounting of attacker and defender costs and penetration probabilities provides cost-utility payoff matrices and penetration probability matrices. These can be used as decision tools by both the defender and attacker. Inspection of the matrices allows players to deduce preferred strategies (or COAs) based on game-theoretical equilibrium solutions. The matrices also help in analyzing anticipated effects of potential human-based choices of wargame strategies and counter-strategies. We describe a mathematical game-theoretic formalism and offer detailed analysis of a table-top cyber wargame executed at the US Army Research Laboratory. Our analysis shows how game-theoretical calculations can provide an effective tool for decision-making during cyber wargames.

Jeffrey Pawlick, Edward Colbert, Quanyan Zhu

Deception plays critical roles in economics and technology, especially in emerging interactions in cyberspace. Holistic models of deception are needed in order to analyze interactions and to design mechanisms that improve them. Game theory provides such models. In particular, existing work models deception using signaling games. But signaling games inherently model deception that is undetectable. In this paper, we extend signaling games by including a detector that gives off probabilistic warnings when the sender acts deceptively. Then we derive pooling and partially-separating equilibria of the game. We find that 1) high-quality detectors eliminate some pure-strategy equilibria, 2) detectors with high true-positive rates encourage more honest signaling than detectors with low false-positive rates, 3) receivers obtain optimal outcomes for equal-error-rate detectors, and 4) surprisingly, deceptive senders sometimes benefit from highly accurate deception detectors. We illustrate these results with an application to defensive deception for network security. Our results provide a quantitative and rigorous analysis of the fundamental aspects of detectable deception.

Jeffrey Pawlick, Edward Colbert, Quanyan Zhu

Cyberattacks on both databases and critical infrastructure have threatened public and private sectors. Ubiquitous tracking and wearable computing have infringed upon privacy. Advocates and engineers have recently proposed using defensive deception as a means to leverage the information asymmetry typically enjoyed by attackers as a tool for defenders. The term deception, however, has been employed broadly and with a variety of meanings. In this paper, we survey 24 articles from 2008-2018 that use game theory to model defensive deception for cybersecurity and privacy. Then we propose a taxonomy that defines six types of deception: perturbation, moving target defense, obfuscation, mixing, honey-x, and attacker engagement. These types are delineated by their information structures, agents, actions, and duration: precisely concepts captured by game theory. Our aims are to rigorously define types of defensive deception, to capture a snapshot of the state of the literature, to provide a menu of models which can be used for applied research, and to identify promising areas for future work. Our taxonomy provides a systematic foundation for understanding different types of defensive deception commonly encountered in cybersecurity and privacy.

Jeffrey Pawlick, Thi Thu Hang Nguyen, Edward Colbert et al.

Advanced persistent threats (APTs) are stealthy attacks which make use of social engineering and deception to give adversaries insider access to networked systems. Against APTs, active defense technologies aim to create and exploit information asymmetry for defenders. In this paper, we study a scenario in which a powerful defender uses honeynets for active defense in order to observe an attacker who has penetrated the network. Rather than immediately eject the attacker, the defender may elect to gather information. We introduce an undiscounted, infinite-horizon Markov decision process on a continuous state space in order to model the defender's problem. We find a threshold of information that the defender should gather about the attacker before ejecting him. Then we study the robustness of this policy using a Stackelberg game. Finally, we simulate the policy for a conceptual network. Our results provide a quantitative foundation for studying optimal timing for attacker engagement in network defense.

Misty Blowers, Jose Iribarne, Edward Colbert et al.

We consider the future cyber security of industrial control systems. As best as we can see, much of this future unfolds in the context of the Internet of Things (IoT). In fact, we envision that all industrial and infrastructure environments, and cyber-physical systems in general, will take the form reminiscent of what today is referred to as the IoT. IoT is envisioned as multitude of heterogeneous devices densely interconnected and communicating with the objective of accomplishing a diverse range of objectives, often collaboratively. One can argue that in the relatively near future, the IoT construct will subsume industrial plants, infrastructures, housing and other systems that today are controlled by ICS and SCADA systems. In the IoT environments, cybersecurity will derive largely from system agility, moving-target defenses, cybermaneuvering, and other autonomous or semi-autonomous behaviors. Cyber security of IoT may also benefit from new design methods for mixed-trusted systems; and from big data analytics -- predictive and autonomous.