1,767 research outputs found
Strategic Learning for Active, Adaptive, and Autonomous Cyber Defense
The increasing instances of advanced attacks call for a new defense paradigm
that is active, autonomous, and adaptive, named as the \texttt{`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 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.Comment: arXiv admin note: text overlap with arXiv:1906.1218
Adversarial Learning of Robust and Safe Controllers for Cyber-Physical Systems
We introduce a novel learning-based approach to synthesize safe and robust
controllers for autonomous Cyber-Physical Systems and, at the same time, to
generate challenging tests. This procedure combines formal methods for model
verification with Generative Adversarial Networks. The method learns two Neural
Networks: the first one aims at generating troubling scenarios for the
controller, while the second one aims at enforcing the safety constraints. We
test the proposed method on a variety of case studies
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