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An Enhanced Resilient Backpropagation Artificial Neural Network for Intrusion Detection System


Reyadh Shaker Naoum, Namh Abdula Abid, Zainab Namh Al-Sultani


Vol. 12  No. 3  pp. 11-16


The potential threats and attacks that can be caused by intrusions have been increased rapidly due to the dependence on network and internet connectivity. In order to prevent such attacks, Intrusion Detection Systems were designed. Different soft computing based methods have been proposed for the development of Intrusion Detection Systems. In this paper a multilayer perceptron is trained using an enhanced resilient backpropagation training algorithm for intrusion detection. In order to increase the convergence speed an optimal or ideal learning factor was added to the weight update equation. The performance and evaluations were performed using the NSL-KDD anomaly intrusion detection dataset. The experiments results demonstrate that the system has promising results in terms of accuracy, storage and time; the designed system was capable to classify records with a detection rate about 94.7%.


Intrusion Detection System, Resilient Backpropagation, Artificial Neural Network