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Intrusion Detection and Ejection Framework Against Lethal Attacks in UAV-Aided Networks: A Bayesian Game-Theoretic Methodology

Abstract : Advances in wireless communications and microelectronics have spearheaded the development of unmanned aerial vehicles (UAVs), which can be used to augment a ground network composed of sensors and/or vehicles in order to increase coverage, enhance the end-to-end delay, and improve data processing. While UAV-aided networks can potentially find applications in many areas, a number of issues, particularly security, have not been readily addressed. The intrusion detection system is the most commonly used technique to detect attackers. In this paper, we focus on addressing two main issues within the context of intrusion detection and attacker ejection in UAV-aided networks, namely, activation of the intrusion monitoring process and attacker ejection. In fact, when a large number of nodes activate their monitoring processes, the incurred overhead can be substantial and, as a consequence, degrades the network performance. Therefore, a tradeoff between the intrusion detection rate and overhead is considered in this work. It is not always the best strategy to eject a node immediately when it exhibits a bad sign of malicious activities since this sign could be provisional (the node may switch to a normal behavior in the future) or be simply due to noise or unreliable communications. Thus, a dilemma between detection and false positive rates is taken into account in this paper. We propose to address these two security issues by a Bayesian game model in order to accurately detect attacks (i.e., high detection and low false positive rates) with a low overhead. Simulation results have demonstrated that our proposed security game framework does achieve reliable detection.
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Submitted on : Tuesday, June 20, 2017 - 7:13:49 PM
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Hichem Sedjelmaci, Sidi Mohammed Senouci, Nirwan Ansari. Intrusion Detection and Ejection Framework Against Lethal Attacks in UAV-Aided Networks: A Bayesian Game-Theoretic Methodology. IEEE Transactions on Intelligent Transportation Systems, IEEE, 2017, 18 (5), pp.1143 - 1153. ⟨10.1109/TITS.2016.2600370⟩. ⟨hal-01543420⟩



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