Cloud Intrusion Detection System Based on SVM

Cloud Intrusion Detection System Based on SVM

Detection system, Machine Learning; network intrusion detection; Cloud computing, SVM, Normal and abnormal behaviors.

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.

Khattab M. Ali Alheeti

Ali Azawii Abdu Lateef

Abdulkareem Alzahrani

Azhar Imran

Duaa Al_Dosary

International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 11 (2023); pp. 101-114

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2023-06-07

Copyright (c) 2023 Haider TH.Salim ALRikabi, Khattab M. Ali Alheeti, Ali Azawii Abdu Lateef, Abdulkareem Alzahrani, Azhar Imran, Duaa Al_Dosary

https://creativecommons.org/licenses/by/4.0

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eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article