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ZoneTrust: Fast Zone-Based Node Compromise Detection and Revocation in Wireless Sensor Networks Using Sequential Hypothesis Testing
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DOI：10.1109/TDSC.2011.65.png)
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Abstract
En 中文
Due to the unattended nature of wireless sensor networks, an adversary can physically capture and compromise sensor nodes and then mount a variety of attacks with the compromised nodes. To minimize the damage incurred by the compromised nodes, the system should detect and revoke them as soon as possible. To meet this need, researchers have recently proposed a variety of node compromise detection schemes in wireless ad hoc and sensor networks. For example, reputation-based trust management schemes identify malicious nodes but do not revoke them due to the risk of false positives. Similarly, software-attestation schemes detect the subverted software modules of compromised nodes. However, they require each sensor node to be attested periodically, thus incurring substantial overhead. To mitigate the limitations of the existing schemes, we propose a zone-based node compromise detection and revocation scheme in wireless sensor networks. The main idea behind our scheme is to use sequential hypothesis testing to detect suspect regions in which compromised nodes are likely placed. In these suspect regions, the network operator performs software attestation against sensor nodes, leading to the detection and revocation of the compromised nodes. Through quantitative analysis and simulation experiments, we show that the proposed scheme detects the compromised nodes with a small number of samples while reducing false positive and negative rates, even if a substantial fraction of the nodes in the zone are compromised. Additionally, we model the detection problem using a game theoretic analysis, derive the optimal strategies for the attacker and the defender, and show that the attacker's gain from node compromise is greatly limited by the defender when both the attacker and the defender follow their optimal strategies.
Keywords:
Node compromise detection
sequential analysis
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