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Jafar Pour, Z., Rad, F., Parvin, H. (2019). Improving Intrusion Detection System Using a New Feature Selection Technique. Electronic and Cyber Defense, 6(4), 143-152.
Zahra Jafar Pour; Farhad Rad; Hamid Parvin. "Improving Intrusion Detection System Using a New Feature Selection Technique". Electronic and Cyber Defense, 6, 4, 2019, 143-152.
Jafar Pour, Z., Rad, F., Parvin, H. (2019). 'Improving Intrusion Detection System Using a New Feature Selection Technique', Electronic and Cyber Defense, 6(4), pp. 143-152.
Jafar Pour, Z., Rad, F., Parvin, H. Improving Intrusion Detection System Using a New Feature Selection Technique. Electronic and Cyber Defense, 2019; 6(4): 143-152.

Improving Intrusion Detection System Using a New Feature Selection Technique

پدافند الکترونیکی و سایبری
Article 12, Volume 6, Issue 4 - Serial Number 24, April 2019, Pages 143-152    PDF (1.18 M)
Document Type: Original Article
Authors
Zahra Jafar Pour; Farhad Rad* ; Hamid Parvin
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Receive Date: 14 January 2018,  Accept Date: 27 May 2018 
Abstract
Intrusion detection is an important subject of research in the cyberspace field. In an Intrusion            DetectionSystem (IDS), redundant and irrelevant features have a negative impact on the IDS performance. Therefore, an appropriate feature selection method is an important part of IDSs for eliminating unrelated and redundant features. In this paper, a new feature selection method is proposed that joins features level to level and step by step to select a subset of proper features in order to finally detect intrusion more            accurately and speedily. The purpose of the proposed method is applying it in intrusion detection systems to distinguish a normal the connection from an intruding connection to the network. The experiments on the NSL-KDD dataset show that the proposed method in comparison with other methods selects only six       important features among the 41 features in the baseline, and can detect an intrusion with precision above 99.58% by relying only on these six features. In other words, the proposed method's failure has been 42 in 10,000 connections of the network and has correctly identified other 9958 regular connections and labeled them as normal. Finally, improvement in the algorithm runtime and the percentage accuracy of the        proposed method in comparison with other methods has been verified and reported.
 
Keywords
Security in cyberspace; Intrusion detection; Classification; Feature selection; anomaly; attack
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