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ADAPT: A Game Inspired Attack-Defense and Performance Metric Taxonomy

Abstract : Game theory has been researched extensively in network security demonstrating an advantage of modeling the interactions between attackers and defenders. Game theoretic defense solutions have continuously evolved in most recent years. One of the pressing issues in composing a game theoretic defense system is the development of consistent quantifiable metrics to select the best game theoretic defense model. We survey existing game theoretic defense, information assurance, and risk assessment frameworks that provide metrics for information and network security and performance assessment. Coupling these frameworks, we propose a game theoretic approach to attack-defense and performance metric taxonomy (ADAPT). ADAPT uses three classifications of metrics: (i) Attacker, (ii) Defender (iii) Performance. We proffer ADAPT with an attempt to aid game theoretic performance metrics. We further propose a game decision system (GDS) that uses ADAPT to compare competing game models. We demonstrate our approach using a distributed denial of service (DDoS) attack scenario.
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Submitted on : Thursday, February 9, 2017 - 5:24:11 PM
Last modification on : Thursday, August 22, 2019 - 2:44:30 PM
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Chris B. Simmons, Sajjan G. Shiva, Harkeerat Singh Bedi, Vivek Shandilya. ADAPT: A Game Inspired Attack-Defense and Performance Metric Taxonomy. 28th Security and Privacy Protection in Information Processing Systems (SEC), Jul 2013, Auckland, New Zealand. pp.344-365, ⟨10.1007/978-3-642-39218-4_26⟩. ⟨hal-01463837⟩



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