Adversarial Deep Reinforcement Learning Based Adaptive Moving Target Defense

Taha Eghtesad, Yevgeniy Vorobeychik, Aron Laszka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Moving target defense (MTD) is a proactive defense approach that aims to thwart attacks by continuously changing the attack surface of a system (e.g., changing host or network configurations), thereby increasing the adversary’s uncertainty and attack cost. To maximize the impact of MTD, a defender must strategically choose when and what changes to make, taking into account both the characteristics of its system as well as the adversary’s observed activities. Finding an optimal strategy for MTD presents a significant challenge, especially when facing a resourceful and determined adversary who may respond to the defender’s actions. In this paper, we propose a multi-agent partially-observable Markov Decision Process model of MTD and formulate a two-player general-sum game between the adversary and the defender. To solve this game, we propose a multi-agent reinforcement learning framework based on the double oracle algorithm. Finally, we provide experimental results to demonstrate the effectiveness of our framework in finding optimal policies.

Original languageEnglish (US)
Title of host publicationDecision and Game Theory for Security - 11th International Conference, GameSec 2020, Proceedings
EditorsQuanyan Zhu, John S. Baras, Radha Poovendran, Juntao Chen
PublisherSpringer Science and Business Media Deutschland GmbH
Pages58-79
Number of pages22
ISBN (Print)9783030647926
DOIs
StatePublished - 2020
Event11th Conference on Decision and Game Theory for Security, GameSec 2020 - College Park, United States
Duration: Oct 28 2020Oct 30 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12513 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th Conference on Decision and Game Theory for Security, GameSec 2020
Country/TerritoryUnited States
CityCollege Park
Period10/28/2010/30/20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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