An Enhanced Resilient Backpropagation Artificial Neural Network for Intrusion Detection System
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 NSLKDD anomaly intrusion detection dataset.