Training a Neural Network for Cyberattack Classification Applications Using Hybridization of an Artificial Bee Colony and Monarch Butterfly Optimization

Arguably the most recurring issue concerning network security is building an approach that is capable of detecting intrusions into network systems. This issue has been addressed in numerous works using various approaches, of which the most popular one is to consider intrusions as anomalies with respect to the normal traffic in the network and classify network packets as either normal or abnormal. Improving the accuracy and efficiency of this classification is still an open problem to be solved. The study carried out in this article is based on a new approach for intrusion detection that is mainly implemented using the Hybrid Artificial Bee Colony algorithm (ABC) and Monarch Butterfly optimization (MBO). This approach is implemented for preparing an artificial neural system (ANN) in order to increase the precision degree of classification for malicious and non-malicious traffic in systems. The suggestion taken into consideration was to place side-by-side nine other metaheuristic algorithms that are used to evaluate the proposed approach alongside the related works. In the beginning the system is prepared in such a way that it selects the suitable biases and weights utilizing a hybrid (ABC) and (MBO). Subsequently the artificial neural network is retrained by using the information gained from the ideal weights and biases which are obtained from the hybrid algorithm (HAM) to get the intrusion detection approach able to identify new attacks. Three types of intrusion detection evaluation datasets namely KDD Cup 99, ISCX 2012, and UNSW-NB15 were used to compare and evaluate the proposed technique against the other algorithms. The experiment clearly demonstrated that the proposed technique provided significant enhancement compared to the other nine classification algorithms, and that it is more efficient with regards to network intrusion detection.