Network Intrusion Detection System Using Random Forest and Decision Tree Machine Learning Techniques

被引：31

作者：

Bhavani, T. Tulasi ^{[1
]}

Rao, M. Kameswara ^{[1
]}

Reddy, A. Manohar ^{[1
]}

机构：

[1] KL Deemed Be Univ, Vaddeswaram 522502, Andhra Pradesh, India

来源：

FIRST INTERNATIONAL CONFERENCE ON SUSTAINABLE TECHNOLOGIES FOR COMPUTATIONAL INTELLIGENCE | 2020年 / 1045卷

关键词：

Machine learning; Random forest; Decision tree;

D O I：

10.1007/978-981-15-0029-9_50

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the network communications, network interruption is the most vital concern these days. The expanding event of the system assaults is a staggering issue for system administrations. Different research works are now directed to locate a successful and productive answer for forestall interruption in the system so as to guarantee to arrange security and protection. Machine learning is a successful investigation device to identify any irregular occasions happened in the system traffic stream. In this paper, a mix of the decision tree and random forest algorithms is proposed to order any strange conduct in the system traffic.

引用

页码：637 / 643

页数：7

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