图网络在线异常检测跨域耦合模型优化方法

Optimization Method for Cross-domain Coupled Graph Online Anomaly Detection Model

图网络在线异常检测模型在导弹系统网络通信模式监控、雷达系统恶意攻击识别,以及战机控制系统网络活动监测等应用领域中发挥着至关重要的作用。该检测模型将图谱域信号处理模型与时域检测模型相耦合,其高阶非线性处理过程给以高精度检测为导向的跨域耦合异常检测的模型优化带来了巨大挑战。针对此问题,提出了一种图网络在线异常检测跨域耦合模型优化方法。该方法关注高阶非线性跨域耦合检测模型处理信号空时相关性,通过对图网络跨域耦合模型处理信号统计特性的精细推导,揭示该检测模型的空时耦合机理及耦合过程对检测性能的影响,为模型中关键参数的选择提供了依据,弥补了该领域以往仅依赖简化模型和经验进行参数选择的不足。仿真及外场试验结果表明:所提模型优化方法在确保图网络异常检测稳健性的同时,显著提高了检测准确率。

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.