基于PSO模式搜索的网络异常检测方法研究  

Research on Network Anomaly Detection Method Based on PSO Pattern Search

作　　者：李伟民[1] Li Weimin(School of Electronics and Information Engineering, Sias International University, Xinzheng Zhengzhou 451100, China)

机构地区：[1]郑州大学西亚斯国际学院电子信息工程学院,河南新郑451150

出　　处：《信息与电脑》2019年第7期171-172,共2页Information & Computer

基　　金：基于机器学习的电动汽车续航里程估计的研究(项目编号:182102210549)

摘　　要：传统网络异常检测方法检测精准度低的问题是目前网络安全网研究的热点,笔者提出基于PSO模式搜索的网络异常检测方法研究。依据IP包收到和发出速度,进行网络异常特征提取,结合实际网络情况构建异常检测模型、数据预处理、剔除干扰数据等完成方案检测流程。通过仿真实验可知,该方法检测精准度较高,可为网络正常运行提供保障。The problem of low accuracy of traditional network anomaly detection method is a hot topic in network security network research. The author proposes a network anomaly detection method based on PSO mode search. According to the IP packet receiving and sending speed, the network anomaly feature extraction is carried out, and the abnormality detection model, data preprocessing, and interference data elimination are constructed according to the actual network conditions. Through simulation experiments, the method has high detection accuracy and can guarantee the normal operation of the network.

关 键 词：PSO模式搜索 网络异常 检测 IP包 模糊神经网络 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]