基于蚁群算法的通信网络节点攻击漏洞安全检测方法

Security Detection Method of Node Attack for Communication Network Based on Ant Colony Algorithm

摘要

针对在通信网络漏洞检测中, 因忽略簇结构信息使其精准度不足的问题, 提出一种基于蚁群算法的通信网络节点攻击漏洞安全检测方法。 首先运用滑动时间窗口分割与时空互相关分析提取低维可解释节点特征, 增强异常模式区分度; 然后借助卷积神经网络对降维特征进行卷积、 池化及展平操作, 并结合 Softmax 分类器判定异常节点概率, 缩小检测范围; 最后基于被动分簇算法划分的簇结构, 采用蚁群算法, 结合簇内节点关系与通信模式, 通过信息素浓度追踪漏洞位置。 实验结果表明, 该方法能精准定位通信网络攻击漏洞, 可全面、 准确评估网络安全状况, 为通信网络漏洞检测提供了有效解决方案。

Abstract

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.

关键词

通信网络 / 异常节点检测 / 漏洞检测 / 蚁群算法 / 安全漏洞弧

Key words

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

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基金资助

南方电网电动汽车服务有限公司网络安全监督运营辅助服务基金资助项目(CG3800022001914650)