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In this paper, we use cognitive computing to build a WSN security threat analysis model using a data-driven approach and conduct an in-depth and systematic study. In this paper, we develop a simulation platform (OMNeT++-based WSN Security Protocol Simulation Platform (WSPSim)) based on OMNeT++ to make up for the shortcomings of current WSN simulation platforms, improve the simulation capability of WSN security protocols, and provide a new technical means for designing and verifying security protocols. The WSPSim simulation platform is used to simulate and analyze typical WSN protocols and verify the effectiveness of the platform. In this paper, we mainly analyze the node malicious behavior by listening and judging the communication behavior of the nodes, and the current trust assessment is given by the security management nodes. When the security management node is rotated, its stored trust value is used as historical trust assessment and current trust assessment together to participate in the integrated trust value calculation, which improves the reliability of node trust assessment; to increase the security and reliability of the management node, a trust value factor and residual energy factor are introduced in the security management node election in the paper. According to the time of management node election, the weights of both are changed to optimize the election. Using the WSPSim simulation platform, a typical WSN protocol is simulated and analyzed to verify the effectiveness of the platform. In this paper, the simulation results of the LEACH protocol with an MD5 hash algorithm and trust evaluation mechanism and typical LEACH protocol as simulation samples are compared; i.e., the correctness of the simulation platform is verified, and it is shown that improving the security of the protocol and enhancing the security and energy efficiency of wireless sensor networks provide an effective solution.

A Data-Driven WSN Security Threat Analysis Model Based on Cognitive Computing