University of Guilan Journal of Mathematical Modeling 2345-394X 9 3 2021 09 01 An intrusion detection system with a parallel multi-layer neural network 437 450 4608 10.22124/jmm.2021.17362.1502 EN 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 Journal Article 2020 08 12 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 98.60\%  on the well-known NSL-KDD dataset and therefore this method can be considered as a new state-of-the-art anomaly-based IDS. https://jmm.guilan.ac.ir/article_4608_6c422be00fed7b4135a706109a9f4fc8.pdf