An intrusion detection system with a parallel multi-layer neural network

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.