Strategic Learning for Active, Adaptive, and Autonomous Cyber Defense

Linan Huang, Quanyan Zhu

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.

Original languageEnglish (US)
Title of host publicationAdaptive Autonomous Secure Cyber Systems
PublisherSpringer International Publishing
Pages205-230
Number of pages26
ISBN (Electronic)9783030334321
ISBN (Print)9783030334314
DOIs
StatePublished - Jan 1 2020

ASJC Scopus subject areas

  • General Computer Science

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