Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense
release_ddz3g2cezza57lhf3tdhtdkwxa
by
Sailik Sengupta, Subbarao Kambhampati
2020
Abstract
The field of cybersecurity has mostly been a cat-and-mouse game with the
discovery of new attacks leading the way. To take away an attacker's advantage
of reconnaissance, researchers have proposed proactive defense methods such as
Moving Target Defense (MTD). To find good movement strategies, researchers have
modeled MTD as leader-follower games between the defender and a
cyber-adversary. We argue that existing models are inadequate in sequential
settings when there is incomplete information about a rational adversary and
yield sub-optimal movement strategies. Further, while there exists an array of
work on learning defense policies in sequential settings for cyber-security,
they are either unpopular due to scalability issues arising out of incomplete
information or tend to ignore the strategic nature of the adversary simplifying
the scenario to use single-agent reinforcement learning techniques. To address
these concerns, we propose (1) a unifying game-theoretic model, called the
Bayesian Stackelberg Markov Games (BSMGs), that can model uncertainty over
attacker types and the nuances of an MTD system and (2) a Bayesian Strong
Stackelberg Q-learning (BSS-Q) approach that can, via interaction, learn the
optimal movement policy for BSMGs within a reasonable time. We situate BSMGs in
the landscape of incomplete-information Markov games and characterize the
notion of Strong Stackelberg Equilibrium (SSE) in them. We show that our
learning approach converges to an SSE of a BSMG and then highlight that the
learned movement policy (1) improves the state-of-the-art in MTD for
web-application security and (2) converges to an optimal policy in MTD domains
with incomplete information about adversaries even when prior information about
rewards and transitions is absent.
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