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Industrial wireless cyber-physical systems are vulnerable to malicious node attacks, for example, clone node attack. The existing clone detection schemes are either based on upper layer observations or physical layer channel state information. The schemes based on upper layer observations are vulnerable to defamation while the schemes based on channel state information perform better against defamation, but are badly affected by channel conditions. This article applies physical layer reputation and back propagation neural network to clone detection, aiming at improving the detection accuracy. The proposed scheme accumulates the physical layer reputations by channel state information and input them to the neural network. The cloud server performs attack detection by group detection first. If a certain group is classified as attacked, the corresponding edge processor will perform attack tracing to identify the specific clone nodes. During the attack tracing stage, multiple reputations of each node is adopted for a comprehensive inspection. Extensive experiments are conducted on the Universal Software Radio Peripheral platform. The numerical results show that the proposed scheme significantly improves the detection accuracy.

Clone Detection Based on BPNN and Physical Layer Reputation for Industrial Wireless CPS