Network Intrusion Detection using Machine Learning Approaches

被引：0

作者：

Hossain, Zakir ^{[1
]}

Sourov, Md Mahmudur Rahman ^{[1
]}

Khan, Musharrat ^{[1
]}

Rahman, Parves ^{[1
]}

机构：

[1] East West Univ, Comp Sci & Engn Dept, Dhaka, Bangladesh

来源：

PROCEEDINGS OF THE 2021 FIFTH INTERNATIONAL CONFERENCE ON I-SMAC (IOT IN SOCIAL, MOBILE, ANALYTICS AND CLOUD) (I-SMAC 2021) | 2021年

关键词：

Categorical feature conversion; classification; data preprocessing; network intrusion detection; !text type='Python']Python[!/text] machine learning tools; supervised learning;

D O I：

10.1109/I-SMAC52330.2021.9640949

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

At present network intrusion is regarded as a great threat in network usage and communication. Network intrusion detection system detects and prevents anomalous activities or attacks in networks. Many classifiers are used to detect network attacks. In this paper, we have evaluated the performance of four popular classifiers, namely, Decision Tree, Support Vector Machine, Random Forest and Naive Bayes on UNSW-NB15 dataset using Python language along with its Pandas and SKlearn libraries. We have used the complete UNSW-NB15 dataset with 43 features. Experimental results have shown improvement of accuracy for Random Forest, Decision Tree and Naive Bayes over previously reported results produced by Apache Spark and its MLlib.

引用

页码：303 / 307

页数：5

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