![Cyber deception (with focus on honeypots):
Evasion risk [Spitzner 2003], Intelligence [Wagener et al. 2009, 2011], Engagement [Pawlick et
al.19], Detection risk [Dowling et al. 2019], Resource consumption [Akiyama et al. 2012], False
positives [Qassrawi & Zhang 2010] , Strategic design [Pawlick et al. 2021], etc.
Compliance and mechanism design:
Insider Threat Mitigation Guide [CISA 2020]; The Critical Role of Positive Incentives for Reducing
Insider Threats [CERT/CMU 2016]
Mitigating inadvertent insider threats with incentives [Liu et al. 2009]; Compliance control [Casey, et
al. 2015], ZETAR [Huang and Zhu 2022]; etc.
Literature](https://image.slidesharecdn.com/koopmanprizepresentationfinal-221027120606-aa63e80a/85/Koopman-Prize-Presentation-18-320.jpg)
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- 1. Duplicity Games for Deception Design With an Application to Insider Threat Mitigation Linan Huang and Quanyan Zhu 2022 INFORMS Annual Conference October 17, 2022
- 7. Failure of Boundary Defense Insider Threats
- 8. Challenge 1: Perimeter defense does not work. Challenges of Insider Threats Solution: Zero-Trust architecture
- 9. Challenge 2: Asymmetric information Challenges of Insider Threats Solution: Cyber deception
- 10. Challenge 3: False positive from negligent legitimate users Challenges of Insider Threats Solution: Compliance and mechanism design
- 11. Game between a defender and an unknown user
- 12. State Feature pattern Protocols Ports Response time Error response 1 1 1 1 2 2 1 Feature Generator 1 2 1 2 Incorporating Cyber Deception into the Game Model
- 13. 1 1 1 1 2 2 1 Users posterior belief (1) 1 (1) (2) 1 (2)
- 14. Users posterior belief (1) 1 (1) Users action Users utility ｐ(＄, ＄＄, ＄) Users type $$ $$ $ $$$
- 15. 1 1 1 1 2 2 1 (1) 1 (1) (2) 1 (2) $$ $$ $ $$$ Feature Generator Belief Manipulator , Incentive Modulator
- 16. There is a need for a theory for Cyber Deception Mechanism Design. Theory can go beyond the design of generator. Belief/Trust Manipulator: e.g., changing honeypot percentage Feature Generator: e.g., configuring honeypots and normal servers Incentive Modulator: e.g., using multi-step authentication How to Design the Generator?
- 17. Duplicity Game for Mechanism Design
- 18. Cyber deception (with focus on honeypots): Evasion risk [Spitzner 2003], Intelligence [Wagener et al. 2009, 2011], Engagement [Pawlick et al.19], Detection risk [Dowling et al. 2019], Resource consumption [Akiyama et al. 2012], False positives [Qassrawi & Zhang 2010] , Strategic design [Pawlick et al. 2021], etc. Compliance and mechanism design: Insider Threat Mitigation Guide [CISA 2020]; The Critical Role of Positive Incentives for Reducing Insider Threats [CERT/CMU 2016] Mitigating inadvertent insider threats with incentives [Liu et al. 2009]; Compliance control [Casey, et al. 2015], ZETAR [Huang and Zhu 2022]; etc. Literature
- 19. Generator Design Problem: Defenders Problem The defender designs a utility-maximizing generator so that the user has no incentive to deviate from the recommendation.
- 20. Dual Formulation: Users Problem The user minimizes his expected effort to satisfy the defenders security objective.
- 21. Users action Users type K actions and M types 情 possible security policies Only (, , ) are enforceable. $$ $$ $ $$$ Always exists one optimal generator that only relies on security policies. Feasibility of Generators: Enforceable Policies
- 22. Incentive Threshold (IT): Uncontrollable if the majority of insiders are adversarial. Deterrence Threshold (DT): Uncontrollable if there is an insufficient number of honeypots. Incentive Threshold Deterrence Threshold How does the percentage or probability of honeypots and (negligent) insiders affect defenders utility?
- 23. If < IT && <DT, then the design of the generator is insufficient to deter an insider. Zero Trust Margin Cannot be incentivized Deterrence Capacity
- 24. Defenders utility structure = users utility structure Defenders utility structure = -users utility structure No deception needed: Reveal full information Maximum deception needed: Reveal zero information Manageability of Incentive Modulator: Designing the Reward
- 25. No deception needed: Reveal full information Maximum deception needed: Reveal zero information Defenders utility structure = users utility structure + Only the sign of matters. > 0 < 0 Manageability under Linearly dependent utility structure
- 26. Principles for Joint Design of GMM Extension to Multi-Dimensional Mechanism Designs The defender can design reward independently. Separation Principle Design Information + Trust = Design Trust Equivalence Principle
- 27. Fast Learning for Finite-Step Mitigation
- 28. Duplicity game for designing cognitive honeypots Strategic and incentive-compatible Insider threat mitigation Enforceability, manageability, and deterrence Online and learning-based implementation Conclusions Huang, L. and Zhu, Q., 2021. Duplicity games for deception design with an application to insider threat mitigation. IEEE Transactions on Information Forensics and Security, 16, pp.4843-4856.
- 29. Five Generations of Security Paradigms 1G-SP: Laissez-Faire Security 2G-SP: Perimeter Security 3G-SP: Reactive Security 4G-SP: Proactive Security 5G-SP: Federated Security Five Generations of Security Paradigms (SPs)
- 30. 4G-SP: Proactive Security 5G-SP: Federated Security Emergence of AI-Powered Attacks Incorporate AI and system science to develop cognitive honeypots for 5G-SP Consolidation of Strategic, Proactive, and Autonomous Defense
- 31. Contact: Quanyan Zhu Email: qz494@nyu.edu