常规的无线局域网络入侵信号检测节点多为独立式设定，检测效率较低，导致入侵信号检测误检率较高，为此提 出对基于GA-SVM算法的无线局域网络入侵信号检测方法。该方法首先采用关联的方式进行入侵信号特征提取，提升检测 效率，设置关联性检测节点，构建GA-SVM测算入侵信号检测模型，采用定位分离方法来实现信号检测处理。测试结果表明： 针对选定的300个采样点进行信号入侵检测，对比于传统分布式光纤网络入侵信号检测组、传统FastICA测算网络入侵信号检 测组，此次所设计的GA-SVM测算网络入侵信号检测组最终得出的入侵信号检测误检率被较好地控制在20%以下，说明基于 GA-SVM算法的检测效果更佳，针对性更强，具有实际的应用价值。

Conventional wireless local area network intrusion signal detection nodes are mostly set independently, resulting in low detection efficiency and high false detection rate of intrusion signal detection. Therefore, this article proposes a wireless local area network intrusion signal detection method based on GA-SVM algorithm. This method first uses correlation to extract intrusion sig‐ nal features, improve detection efficiency, set up correlation detection nodes, construct a GA-SVM intrusion signal detection model, and use location separation method to achieve signal detection processing. The test results show that for signal intrusion detection at the selected 300 sampling points, compared to the traditional distributed fiber optic network intrusion signal detection group and the traditional FastICA calculation network intrusion signal detection group, the GA-SVM calculation network intrusion signal detection group has a good detection error rate of less than 20%, indicating that with the assistance of the GA-SVM algorithm, the current de‐ sign has better detection results, stronger pertinence, and practical application value.