In the detection of communication network vulnerabilities, the accuracy is insufficient due to the lack of cluster structure information. Therefore, a security detection method for communication network node attack vulnerabilities based on ant colony algorithm is studied. Firstly, sliding time window segmentation and spatiotemporal cross-correlation analysis are used to extract low dimensional interpretable node features and enhance anomaly pattern discrimination. Then, convolutional neural networks are used to perform convolution, pooling, and flattening operations on the reduced dimensional features, combined with a Softmax classifier to determine the probability of abnormal nodes and narrow down the detection range. Finally, based on the passive clustering algorithm, the cluster structure is divided using ant colony algorithm, combined with the relationships

and communication modes of nodes within the cluster, to track the location of vulnerabilities through pheromone concentration. The experimental results show that this method can accurately locate communication network attack vulnerabilities. Therefore, this method can comprehensively and accurately evaluate the network security situation, providing an effective solution for communication network vulnerability detection.

communication network, abnormal node detection, vulnerability detection, ant colony, security vulnerability arc