Visible to the public GUIDEX: A Game-Theoretic Incentive-Based Mechanism for Intrusion Detection NetworksConflict Detection Enabled

TitleGUIDEX: A Game-Theoretic Incentive-Based Mechanism for Intrusion Detection Networks
Publication TypeJournal Article
Year of Publication2012
AuthorsQuanyan Zhu, University of Illinois at Urbana-Champaign, Carol Fung, Raouf Boutaba, Tamer Başar, University of Illinois at Urbana-Champaign
JournalIEEE Journal on Selected Areas in Communications
Volume30
Issue11
Date Published12/2012
Keywordscollaborative networks, game theory, incentive compatitility, Intrusion Detection Systems, network optimization, network security and economics, NSA, NSA SoS Lablets Materials, science of security, Toward a Theory of Resilience in Systems: A Game-Theoretic Approach
Abstract

Traditional intrusion detection systems (IDSs) work in isolation and can be easily compromised by unknown threats. An intrusion detection network (IDN) is a collaborative IDS network intended to overcome this weakness by allowing IDS peers to share detection knowledge and experience, and hence improve the overall accuracy of intrusion assessment. In this work, we design an IDN system, called GUIDEX, using gametheoretic modeling and trust management for peers to collaborate truthfully and actively. We first describe the system architecture and its individual components, and then establish a gametheoretic framework for the resource management component of GUIDEX. We establish the existence and uniqueness of a Nash equilibrium under which peers can communicate in a reciprocal incentive compatible manner. Based on the duality of the problem, we develop an iterative algorithm that converges geometrically to the equilibrium. Our numerical experiments and discrete event simulation demonstrate the convergence to the Nash equilibrium and the security features of GUIDEX against free riders, dishonest insiders and DoS attacks

Citation Keynode-31862

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GUIDEX A Game-Theoretic Incentive-Based Mechanism for Intrusion Detection Networks