Network Intrusion Detection Using Linear and Ensemble ML Modeling

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Author(s): Shilpi Hiteshkumar Parikh (U and P U. Patel Department of Computer Engineering, CSPIT, Charotar University of Science and Technology (CHARUSAT), Changa, India), Anushka Gaurang Sandesara (U and P U. Patel Department of Computer Engineering, CSPIT, Charotar University of Science and Technology (CHARUSAT), Changa, India)and Chintan Bhatt (U and P U. Patel Department of Computer Engineering, CSPIT, Charotar University of Science and Technology (CHARUSAT), Changa, India)
Copyright: 2022
Pages: 24
Source title: Implementing Data Analytics and Architectures for Next Generation Wireless Communications
Source Author(s)/Editor(s): Chintan Bhatt (Charotar University of Science and Technology, India), Neeraj Kumar (Thapar University, India), Ali Kashif Bashir (Manchester Metropolitan University, UK)and Mamoun Alazab (Charles Darwin University, Australia)
DOI: 10.4018/978-1-7998-6988-7.ch003

Keywords: Information Science Reference / Media & Communications / Networking and Telecommunications / Wireless Systems

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Abstract

Network attacks are continuously surging, and attackers keep on changing their ways in penetrating a system. A network intrusion detection system is created to monitor traffic in the network and to warn regarding the breach in security by invading foreign entities in the network. Specific experiments have been performed on the NSL-KDD dataset instead of the KDD dataset because it does not have redundant data so the output produced from classifiers will not be biased. The main types of attacks are divided into four categories: denial of service (DoS), probe attack, user to root attack (U2R), remote to local attack (R2L). Overall, this chapter proposes an intense study on linear and ensemble models such as logistic regression, stochastic gradient descent (SGD), naïve bayes, light GBM (LGBM), and XGBoost. Lastly, a stacked model is developed that is trained on the above-mentioned classifiers, and it is applied to detect intrusion in networks. From the plethora of approaches taken into consideration, the authors have found maximum accuracy (98.6%) from stacked model and XGBoost.

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Network Intrusion Detection Using Linear and Ensemble ML Modeling

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