Examining Machine Learning Models Toward Green Network Intrusion Detection

Ammar Abdallah; Bashar Abdallah; Munthir Qasaimeh; Rola Al Bataineh; Malik Qasaimeh

doi:10.15866/irecap.v13i6.24482

Examining Machine Learning Models Toward Green Network Intrusion Detection

Ammar Abdallah^(1*), Bashar Abdallah⁽²⁾, Munthir Qasaimeh⁽³⁾, Rola Al Bataineh⁽⁴⁾, Malik Qasaimeh⁽⁵⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/irecap.v13i6.24482

Abstract

Network intrusion detection can affect businesses by protecting sensitive information, reducing the risk of data breaches, and complying with regulations and industry standards. The continuous expansion of the Internet has led to an increase in the number of cyber-attacks, with ransomware and zero-day exploits becoming more prevalent. Despite the usefulness of traditional network security methods, machine learning techniques are becoming more viable candidates in the current security environment owing to their promising results in anomaly identification. The aim of this study is to examine machine learning models for network intrusion detection and provide insights for the development of green and sustainable intrusion detection systems. This was accomplished by implementing a diverse set of supervised learning algorithms on the KDD Cup 99 dataset. Our findings indicate that the random tree and random forest tree outperformed the other algorithms, demonstrating their effectiveness in both intrusion detection and green and sustainable AI.
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Keywords

Machine Learning; Network Intrusions; Supervised Learning; Green and Sustainable AI

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