| Title | Optimal Timing in Dynamic and Robust Attacker Engagement During Advanced Persistent Threats |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Pawlick, Jeffrey, Nguyen, Thi Thu Hang, Colbert, Edward, Zhu, Quanyan |
| Conference Name | 2019 International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT) |
| Keywords | advanced persistent threat, APT, APT attack, attacker engagement, Games, Human Behavior, Markov Decision Process, Markov processes, Metrics, pubcrawl, resilience, Resiliency, Robustness, Scalability, security, Sensors, Silicon, Stackelberg game, Timing |
| Abstract | 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. |
| DOI | 10.23919/WiOPT47501.2019.9144123 |
| Citation Key | pawlick_optimal_2019 |
Optimal Timing in Dynamic and Robust Attacker Engagement During Advanced Persistent Threats