图网络在线异常检测跨域耦合模型优化方法

The graph online anomaly detection model plays a vital role in a wide range of application fields, including the network communication mode monitoring of missile system, the malicious attack identification of radar system, and the network activity monitoring of fighter aircraft control system. The detection model couples the spectral domain signal processing model with the time domain detection model, which involves the high-order nonlinear signal processing and introduces the space-time correlation, posing a significant challenge in achieving the robust and high-precision detection through the optimization of cross-domain coupled graph online anomaly detection model. An optimization method is proposed for the cross-domain coupled graph online anomaly detection model. The spatial-temporal signal correlation generated during signal processing is considered in the proposed optimization method. The spatial-temporal coupling mechanism and the impact of coupling process on detection performance are studied by intricately deriving the statistical characteristics, providing the basis for selecting the key parameter values in the anomaly detection model, and addressing the disadvantage of relying solely on approximation and empirical methods for parameter selection. Simulated results demonstrate that the proposed optimization method enhances detection accuracy while preserving the robustness of anomaly detection within graph networks.