Cloud Intrusion Detection System Based on SVM

被引：2

作者：

Alheeti K.M.A. ^{[1
]}

Lateef A.A.A. ^{[2
]}

Alzahrani A. ^{[3
]}

Imran A. ^{[4
]}

Al Dosary D. ^{[1
]}

机构：

[1] Computer Networking Systems Department, University of Anbar, Anbar

[2] Human Resources Department, University of Anbar, Anbar

[3] Computer Engineering and Science Department, Al Baha University, Al Baha

[4] Department of Creative Technologies, Air University, Islamabad

来源：

International Journal of Interactive Mobile Technologies | 2023年 / 17卷 / 11期

关键词：

Cloud computing; detection system; Machine Learning; network intrusion detection; normal and abnormal behaviors; SVM;

D O I：

10.3991/ijim.v17i11.39063

中图分类号：

学科分类号：

摘要：

The demand for better intrusion detection and prevention solutions has elevated due to the current global uptick in hacking and computer network attacks. The Intrusion Detection System (IDS) is essential for spotting network attacks and anomalies, which have increased in size and scope. A detection system has become an effective security method that monitors and investigates security in cloud computing. However, several existing methods have faced issues such as low classification accuracy, high false positive rates, and low true positive rates. To solve these problems, a detection system based on Support Vector Machine (SVM) is proposed in this paper. In this method, the SVM classifier is utilized for network data classification into normal and abnormal behaviors. The Cloud Intrusion Detection Dataset is used to test the effectiveness of the suggested system. The experimental results show which the suggested system can detect abnormal behaviors with high accuracy. © 2023, International Journal of Interactive Mobile Technologies. All Rights Reserved.

引用

页码：101 / 114

页数：13

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