基于随机森林算法和K-means算法的网络攻击识别方法  

Network Attack Identification Method Based on Random Forest Algorithm and K-means

作　　者：荣文晶 高锐[1,2] 赵弘洋 云雷[1,2] 彭辉 RONG Wenjing;GAO Rui;ZHAO Hongyang;YUN Lei;PENG Hui(CEPREI,Guangzhou 511370,China;The Ministry of Industry and Information Technology Key Laboratory of General Quality Technology and Application of Intelligent Manufacturing,Guangzhou 511370,China)

机构地区：[1]工业和信息化部电子第五研究所,广东广州511370 [2]智能制造装备通用质量技术及应用工业和信息化部重点实验室,广东广州511370

出　　处：《电子产品可靠性与环境试验》2024年第1期8-12,共5页Electronic Product Reliability and Environmental Testing

基　　金：广州市基础研究计划项目——物联网感知层节点隐私保护技术研究(202201010423)资助。

摘　　要：5G网络与核电的深度融合能够提升核电厂生产安全管控水平,减少人为事故,促进核电行业安全和经济发展。但由于网络的接入,为核电安全生产带来了一定的安全风险,恶意攻击者会通过向核电5G网络发起攻击进而破坏核电生产。为了解决核电5G网络场景下面临的网络异常和恶意攻击的问题,提出了一种在核电5G网络场景下基于随机森林算法和K-means算法的实时网络异常检测和网络攻击识别方法,对于提高核电网络安全具有重要的意义。The deep integration of 5G networks and nuclear power can improve the production safety control level of nuclear power plants,reduce man-made accidents,and promote safety and economic development of nuclear power industry.However,due to the access to network,it brings certain security risks for the safe production of nuclear power,and malicious attackers will destroy the nuclear power production by launching attacks to the 5G network of nuclear power production.In order to solve the problems of network anomalies and malicious attacks faced in nuclear power 5G network scenarios,a real-time network anomaly detection and network attack recognition method based on the random forest algorithm and K-means algorithm in nuclear power 5G network scenarios is proposed,which is of great significance to improve nuclear power network security.

关 键 词：随机森林算法 K-MEANS算法 网络异常检测 网络攻击识别 

分 类 号：TP393.081[自动化与计算机技术—计算机应用技术]