An artificial intelligence-based power grid intrusion detection system

Abstract

The application relates to the technical field of data processing, and discloses a power grid intrusion detection system based on artificial intelligence, which comprises a three-dimensional data cube construction module, a three-dimensional data cube feature extraction module, a feature fusion module, an attack probability prediction module, an attack credibility evaluation module and an intrusion protection module. Time stamps between communication traffic, equipment states and node topological relations of the power grid are aligned to generate a three-dimensional data cube of the power grid; abnormal node propagation features and traffic time sequence modes are extracted; time features of the abnormal node propagation features and the traffic time sequence modes are fused through a cross-channel attention mechanism to obtain a fusion feature vector; the fusion feature vector is input into a full connection layer to obtain an attack probability; when the attack probability exceeds a preset threshold, a sandbox verification process is started to generate an attack credibility score; and network isolation and channel switching instructions are issued based on the attack credibility score. The application can improve the accuracy of power grid intrusion detection.

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