04.2021.253.262

Using A Hybrid Algorithm and Feature Selection for Network Anomaly Intrusion Detection

Author(s):

Ali Hussein Shamman Al-Safi†, Zaid Ibrahim Rasool Hani†, Musaddak M. Abdul Zahra†,‡*

Affiliation(s):

† Computer Techniques Engineering Department, Al-Mustaqbal University College, Hilla, Babil, Iraq
‡ Electrical Engineering Department, College of Engineering, University of Babylon, Hilla, Babil, Iraq.

Corresponding Author Email: musaddaqmahir@mustaqbal-college.edu.iq

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Todays, networks security of has become the important problem in each distributed system. A lot of attacks are becoming less able to detect with software of antivirus and firewall. For improving the security, intrusion detection systems (IDSs) are utilized for detecting the anomalies in traffic of network. Network anomaly detection issue is determining, if incoming traffic of network is anomalous/ legitimate. The automated system of detection schemed for identifying the incoming anomalous patterns of traffic usually apply widely utilized techniques of machine learning. In the article, we have utilized the Information Gain- based algorithm. The algorithm chooses the features optimal number from dataset of NSL-KDD. Additionally, we have integrated selection of feature with the technique of machine learning namely as Support Vector Machine (SVM) by utilizing the algorithm of artificial bee colony as well as Optimization-Cuckoo Search Algorithm for optimizing SVM hyper parameters for dataset effective classification. Proposed method performance has been assessed on the modern intrusion dataset as NSLKDD. Experimental results show that the proposed method outperforms also achieves high accuracy in comparison to the other modern techniques in NSLKDD.