Using Ensemble Techniques Based on Machine and Deep Learning for Solving Intrusion Detection Problems: A Survey

Hadeel Qasem Gheni, Department of Software, Information Technology College, University of Babylon, Babylon, Iraq,Follow
Wathiq Laftah Al-Yaseen, Karbala Technical Institute, Al-Furat Al-Awsat Technical University, 56001, Karbala, Iraq

Abstract

Obviously, the increasing threats to network security, which led to devastating network attacks, have taken a heavy toll on enterprises as a simple firewall cannot prevent complex and changing attacks. Therefore, companies should use intrusion detection systems in combination with other security devices to protect against corporate network security issues. In fact, intrusion detection is a system whose primary function is to protect network security by monitoring traffic, collecting and analyzing information, and then issuing an alert in cases where the output of the analysis represents a threat to network security. Intrusion Detection Systems (IDS) can stop unauthorized activity on a network or operating system, react automatically, stop the intrusion's source in time, record it, and alert the network administrator to ensure maximum system security. The process of detecting attacks using a single algorithm has not proven its worth. Therefore, several algorithms were used together by using ensemble learning. To elaborate, ensemble learning is a well-known predictive technique that involves training multiple algorithms to treat the same problem, after which the results are combined to produce a single, potent prediction that can provide performance better than that of a single algorithm. The primary goal of this study is to present an overview of the main ensemble techniques that are used to enhance the effectiveness of the intrusion detection system, as well as the research using these methods as published by Elsevier and Springer from 2018 until the time being. The results prove that the two easiest methods within ensemble learning to implement are majority voting and weighted averaging, which provide good results in terms of accuracy. In cases where the base models have a significant variance, the bagging method would be more beneficial, while the boosting method would be used in cases where the basic models are biased, and in order to lower bias by learning different algorithms, the stacking ensemble methods are used.

Recommended Citation

Gheni, Hadeel Qasem and Al-Yaseen, Wathiq Laftah (2023) "Using Ensemble Techniques Based on Machine and Deep Learning for Solving Intrusion Detection Problems: A Survey," Karbala International Journal of Modern Science: Vol. 9 : Iss. 1 , Article 5.
Available at: https://doi.org/10.33640/2405-609X.3277