Journal of Advanced Defense Science & Technology

Journal of Advanced Defense Science & Technology

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
Jalil Mazloum 1
hamid bigdeli 2
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
Intrusion Detection System
Feature Selection
Mutual Information
Anova F-Test
Genetic Algorithm
Subjects

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Journal of Advanced Defense Science & Technology
Volume 13, Issue 2 - Serial Number 48
Summer
Autumn 2022
Pages 89-99

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