Multi-Traffic Features Network Intrusion Detection Algorithm Based on C4.5

Jingwen Zhou, Xinnan Jiang, Changan Liu, Jing Zhang, Lingling Liao, Jiazhong Lu
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引用次数: 2

Abstract

With more and more network attacks on the Internet, this article uses the C4.5 decision tree classification algorithm to detect 7 types of network attacks from the perspective of network traffic. Firstly, the data is preprocessed, then extract 62 features, finally use our proposed C4.5 divided algorithm for traffic detection. This experiment uses the public data set CSE-CIC-IDS2018 for verification. The experimental results show that the method in this article can effectively detect different types of cyber attacks. The accuracy rate can reach 96.7%, and the false positive rate is only 4.5%.
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基于C4.5的多流量特征网络入侵检测算法
随着互联网上的网络攻击越来越多,本文采用C4.5决策树分类算法,从网络流量的角度对7种网络攻击进行检测。首先对数据进行预处理,提取出62个特征,最后采用本文提出的C4.5分割算法进行流量检测。本实验使用公共数据集CSE-CIC-IDS2018进行验证。实验结果表明,本文方法能够有效检测不同类型的网络攻击。准确率可达96.7%,假阳性率仅为4.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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