Network Intrusion Detection in Computer Networks Using an Efficacious Combined Feature Selection Technique Based on the Intersection Method of Mutual Information, Anova F-Test and Genetic Algorithm

Document Type : -

Authors

1 Department of Electrical Engineering, Shahid Sattari Aeronautical University of Science and Technology

2 AJA Command & Staff University

Abstract

The intrusion detection system (IDS) manages a massive volume of data that comprises irrelevant and redundant features, leading to more significant resource consumption, long-time training and testing procedures, and low detection rate. Hence, feature selection is a crucial phase in intrusion detection. The aim of this paper is to introduce an intersection-based strategy that optimally selects the features for classification. This feature selection involves an intersection of simultaneous mutual information based on the transductive model (MIT-MIT), Anova F-test, and genetic algorithm (GA) methods. A benchmark dataset, named NSL-KDD, is applied to evaluate the effectiveness of the proposed approach. This study includes accuracy, precision, recall, and F1 score as the evaluation metrics for IDS, which analyzes the proposed method with state-of-the-art classifiers. The evaluation results confirm that our feature selection algorithm provides more essential features for IDS to achieve high accuracy, outperforming other comparative algorithms.

Keywords

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References

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History

  • Receive Date: 21 February 2022
  • Revise Date: 19 July 2022
  • Accept Date: 07 August 2022
  • Publish Date: 22 September 2022

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