An intrusion detection system with a parallel multi-layer neural network
Publish place: Journal of Mathematical Modeling، Vol: 9، Issue: 3
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JMMO-9-3_008
تاریخ نمایه سازی: 19 خرداد 1403
Abstract:
Intrusion detection is a very important task that is responsible for supervising and analyzing the incidents that occur in computer networks. We present a new anomaly-based intrusion detection system (IDS) that adopts parallel classifiers using RBF and MLP neural networks. This IDS constitutes different analyzers each responsible for identifying a certain class of intrusions. Each analyzer is trained independently with a small category of related features. The proposed IDS is compared extensively with existing state-of-the-art methods in terms of classification accuracy . Experimental results demonstrate that our IDS achieves a true positive rate (TPR) of ۹۸.۶۰\% on the well-known NSL-KDD dataset and therefore this method can be considered as a new state-of-the-art anomaly-based IDS.
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Authors
Mohammad Hassan Nataj Solhdar
Shohadaye Hoveizeh University of Technology, Dasht-e Azadegan, Khuzestan, Iran
Mehdi Janinasab Solahdar
Islamic Azad University, Mahalat Branch, Mahalat, Iran
Sadegh Eskandari
Department of Computer Science, University of Guilan, Rasht, Iran