Distance and Clustered Feature Selection for the Pattern Recognition of Intrusion Detection in Communication Networks

Authors

  • N. Chitra Research Scholar, Department of Electronics and Communication Engineering, SOE, Presidency University, Bangalore, India
  • Safinaz S. Associate Professor, Department of Electronics and Communication Engineering, SOE,Presidency University, Bangalore, India

Keywords:

Distance-clustered feature selection, classifier, feature selection, mutual Information, true positive rate, accuracy, intrusion detection System, C4, 5 classifier

Abstract

Information related to various types of sensor networks, satellite networks, and communication networks stores a large pool of data in the cloud increasing its usage. As more people access the data, traffic increases and intrusion into the system and detection are unavoidable and can be avoided using intrusion detection system (IDS) to avoid attacks. The effectiveness of IDS is improved by giving the input which has no noise or attacks so that the performance can be improvised. The feature selection concept which reduces the noise and gives the better objects can be deployed in the IDS which make the system to work smoothly in the network. The proposed algorithm Distance- clustered feature selection is based on the distance between the features and proves the effectiveness of Distance- clustered feature selection (DCFS) using C4.5 classifiers, taking the KDDCUP 99 dataset as input. Modified mutual information feature selection algorithm (MMIFSA), dynamic mutual information feature selection algorithm(DMIFSA), and redundant penalty feature mutual information algorithm (RPFMI) models are simulated using the KDDCUP 99 dataset, and their outcomes in comparison with Distance- clustered feature selection are observed. The distance- clustered feature selection outcomes, when compared with the other mutual information-based feature selection algorithms, proved to be more effective. Various performance metrics to build the distance- clustered feature selection model are evaluated and has shown better improvement in TPR and accuracy of 99.948%, indicating the proposed algorithm is effective and all the parameters are improved compared to other algorithms.

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References

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Published

30.11.2023

How to Cite

Chitra, N. ., & S., S. . (2023). Distance and Clustered Feature Selection for the Pattern Recognition of Intrusion Detection in Communication Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(6s), 69–75. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/3940

Issue

Vol. 12 No. 6s (2024)

Section

Research Article

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