Active cyber defense with denial and deception: A cyber-wargame experiment

Heckman, Kristin E.; Walsh, Michael J.; Stech, Frank J.; O'boyle, Todd A.; Dicato, Stephen R.; Herber, Audra F.

doi:10.1016/j.cose.2013.03.015

Cited by 40 publications

(25 citation statements)

References 5 publications

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“…Note that the left hand side of (27) is a symmetric idempotent matrix, however, the right hand side is not necessarily an idempotent matrix. • The results would hold if the message space was larger than p since it would also lead to the same constraint set Ψ (20). However, the derivations would not carry over if S had access to noisy version of the state instead of the actual state.…”

Section: Equilibrium Achieving Signaling Rulesmentioning

confidence: 95%

Hierarchical multistage Gaussian signaling games in noncooperative communication and control systems

2019

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We analyze in this paper finite horizon hierarchical signaling games between (information provider) senders and (decision maker) receivers in a dynamic environment. The underlying information evolves in time while sender and receiver interact repeatedly. Different from the classical communication (control) models, however, the sender (sensor) and the receiver (controller) have different objectives and there is a hierarchy between the players such that the sender leads the game by announcing his policies beforehand. He needs to anticipate the reaction of the receiver and the impact of the actions on the horizon while controlling the transparency of the disclosed information at each interaction. With quadratic cost functions and multi-variate Gaussian processes, evolving according to first order auto-regressive models, we show that memoryless linear sender signaling rules are optimal (in the sense of game-theoretic hierarchical equilibrium) within the general class of strategies. We also show that, again in the non-cooperative control context, linear signaling rules and correspondingly linear control input yield the hierarchical equilibrium within the general class of strategies.

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Section: Equilibrium Achieving Signaling Rulesmentioning

confidence: 95%

Hierarchical multistage Gaussian signaling games in noncooperative communication and control systems

2019

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“…In one real-time red-team versus blue-team cyber war game experiment, a honeypot system failed to deny red-team hackers access to the command and control mission system, but decoys and disinformation did succeed in preventing the adversary from obtaining sensitive data. 83 Importantly, network defense and deception are not just conducted by mindless algorithms. Law enforcement agents and computer security experts are tactically active in the cyber defense ecosystem.…”

Section: Engineering Deceptionmentioning

confidence: 99%

Weaving Tangled Webs: Offense, Defense, and Deception in Cyberspace

Gartzke¹,

Lindsay²

2015

Security Studies

108

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“…The entropy regulation terms in (10) and (11) result in a closed form of strategies and learning dynamics in (13). Without the closed form, distributed learners can adopt general learning schemes which combine the payoff and the strategy update as stated in [46].…”

Section: Heterogeneous and Hybrid Learningmentioning

confidence: 99%

Strategic Learning for Active, Adaptive, and Autonomous Cyber Defense

Huang

Zhu

2020

Adaptive Autonomous Secure Cyber Systems

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The increasing instances of advanced attacks call for a new defense paradigm that is active, autonomous, and adaptive, named as the '3A' defense paradigm. This chapter introduces three defense schemes that actively interact with attackers to increase the attack cost and gather threat information, i.e., defensive deception for detection and counter-deception, feedback-driven Moving Target Defense (MTD), and adaptive honeypot engagement. Due to the cyber deception, external noise, and the absent knowledge of the other players' behaviors and goals, these schemes possess three progressive levels of information restrictions, i.e., from the parameter uncertainty, the payoff uncertainty, to the environmental uncertainty. To estimate the unknown and reduce the uncertainty, we adopt three different strategic learning schemes that fit the associated information restrictions. All three learning schemes share the same feedback structure of sensation, estimation, and actions so that the most rewarding policies get reinforced and converge to the optimal ones in autonomous and adaptive fashions. This work aims to shed lights on proactive defense strategies, lay a solid foundation for strategic learning under incomplete information, and quantify the tradeoff between the security and costs.Recent instances of WannaCry ransomware, Petya cyberattack, and Stuxnet malware have demonstrated the trends of modern attacks and the corresponding new security challenges as follows.

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Active cyber defense with denial and deception: A cyber-wargame experiment

Cited by 40 publications

References 5 publications

Hierarchical multistage Gaussian signaling games in noncooperative communication and control systems

Hierarchical multistage Gaussian signaling games in noncooperative communication and control systems

Weaving Tangled Webs: Offense, Defense, and Deception in Cyberspace

Strategic Learning for Active, Adaptive, and Autonomous Cyber Defense

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