A new approach for intrusion detection system based on training multilayer perceptron by using enhanced Bat algorithm

摘要

The most pressing issue in network security is the establishment of an approach that is capable of detecting violations in computer systems and networks. There have been several efforts for improving it from various points of view. One example is the improvement of the classification of packets on the network, which is imperative in detecting abnormal traffic and hence any potential intrusion. Thus, this study proposes a new approach for intrusion detection that is implemented using an enhanced Bat algorithm (EBat) for training an artificial neural network. The goal of the current study is to increase the accuracy of the classification for malicious and un-malicious network traffic. The proposed study herein includes a comparison with nine other metaheuristic algorithms (conventional and new algorithms) that are used to evaluate the new approach alongside the related works. Firstly, the EBat algorithm was developed and used to select suitable weights and biases. Next, the neural network was employed using the found optimal weights and biases to realize the intrusion detection approach. Four types of intrusion detection evaluation datasets were used to compare the proposed approach against the other algorithms. The findings revealed that the proposed method outperformed the other nine classification algorithms and it is unparalleled for the network intrusion detection.