Abstract
With the proliferation of technologies such as the Internet of Things, Cloud computing, and Social Networking, large quantities of network traffic and data are generated, necessitating the use of effective Intrusion Detection Systems (IDS). IDS play an essential role in detecting hazards within a system and generating alerts for potential attacks. This paper examines the use of machine learning algorithms for intrusion detection with the NSL KDD dataset. However, not all features contribute equally to performance enhancement in large datasets. Therefore, it becomes essential to reduce the feature set to a subset that improves both speed and accuracy. Within the IDS framework, we employ machine learning algorithms such as Random Forest, Support Vector Machine, K Nearest Neighbour, and Decision Tree through rigorous experimentation. Recursive Feature Elimination and Resampling techniques are utilised to select and evaluate the impact of feature reduction. The comparative evaluation of the model’s performance is demonstrated, emphasising the performance enhancements attained through feature selection.
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Abhale, A.B., Avulapalli, J.R. Enhancing intrusion detection recursive feature elimination with resampling in WSN. Int J Syst Assur Eng Manag 14, 2642–2660 (2023). https://doi.org/10.1007/s13198-023-02128-3
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DOI: https://doi.org/10.1007/s13198-023-02128-3