Abstract
Wireless sensor networks face threats of selective forwarding attacks which are simple to implement but difficult to detect. It is difficult to distinguish between malicious packet dropping and the normal packet loss on unstable wireless channels. For this situation, a selective forwarding attack detection method is proposed based on adaptive learning automata and communication quality; the method can eliminate the impact of normal packet loss on selective forwarding attack detection and can detect ordinary selective forwarding attack and special cases of selective forwarding attack. The current and comprehensive communication quality of nodes are employed to reflect the short- and long-term forwarding behaviors of nodes, and the normal packet loss caused by unstable channels and medium-access-control layer collisions is considered. The adaptive reward and penalty parameters of a detection learning automata are determined by the comprehensive communication quality of the node and the voting of its neighbors to reward normal nodes or punish malicious ones. Simulation results indicate the effectiveness of the proposed method in detecting ordinary selective forwarding attacks, black-hole attacks, on-off attacks, and energy exhaustion attacks. In addition, the communication overhead of the method is lower than that of other methods.
Highlights
Wireless sensor networks (WSNs) have been widely used in various fields for the purpose of event monitoring and data gathering, including environment monitoring, forest fire monitoring, traffic data collection, and battlefield data gathering.[1,2,3] many WSNs are deployed in harsh or hostile environments that are open and dangerous for the operation of the networks
It is necessary to design a flexible selective forwarding attack (SFA) detection mechanism with adaptive parameters that takes the normal packet loss caused by unstable channels and medium access control (MAC) layer collisions into account to eliminate the impact of normal packet loss on SFA detection
The proportion of malicious nodes was set to 30%, and the SFA packet-dropping rate was set to 50%
Summary
Wireless sensor networks (WSNs) have been widely used in various fields for the purpose of event monitoring and data gathering, including environment monitoring, forest fire monitoring, traffic data collection, and battlefield data gathering.[1,2,3] many WSNs are deployed in harsh or hostile environments that are open and dangerous for the operation of the networks. This seriously affects data collection and data fusion, especially for data-centric networks. The ability to discover an SFA is useful and necessary for the security, reliability, and energy efficiency of WSNs
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More From: International Journal of Distributed Sensor Networks
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