Visible to the public Game-Theoretic Analysis of Node Capture and Cloning Attack with Multiple Attackers in Wireless Sensor NetworksConflict Detection Enabled

TitleGame-Theoretic Analysis of Node Capture and Cloning Attack with Multiple Attackers in Wireless Sensor Networks
Publication TypeConference Paper
Year of Publication2012
AuthorsQuanyan Zhu, University of Illinois at Urbana-Champaign, Linda Bushnell, Tamer Başar, University of Illinois at Urbana-Champaign
Conference Name51st IEEE Conference on Decision and Control
Date Published12/2012
PublisherIEEE Computer Society
Conference LocationMaui, Hawaii
KeywordsNSA SoS Lablets Materials, science of security, Toward a Theory of Resilience in Systems: A Game-Theoretic Approach, UIUC
Abstract

Wireless sensor networks are subject to attacks such as node capture and cloning, where an attacker physically captures sensor nodes, replicates the nodes, which are deployed into the network, and proceeds to take over the network. In this paper, we develop models for such an attack when there are multiple attackers in a network, and formulate multi-player games to model the noncooperative strategic behavior between the attackers and the network. We consider two cases: a static case where the attackers' node capture rates are time-invariant and the network's clone detection/revocation rate is a linear function of the state, and a dynamic case where the rates are general functions of time. We characterize Nash equilibrium solutions for both cases and derive equilibrium strategies for the players. In the static case, we study both the single-attacker and the multi-attacker games within an optimization framework, provide conditions for the existence of Nash equilibria and characterize them in closed forms. In the dynamic case, we study the underlying multi-person differential game under an open-loop information structure and provide a set of conditions to characterize the open-loop Nash equilibrium. We show the equivalence of the Nash equilibrium for the multi-person game to the saddle-point equilibrium between the network and the attackers as a team. We illustrate our results with numerical examples.

Citation Keynode-31860