A new evolutionary neural networks based on intrusion detection systems using locust swarm optimization

The need to avoid computer system breaches is increasing. Many researchers have adopted different approaches, such as intrusion detection systems (IDSs), to handle various threats. Intrusion detection has become an imperative system to detect various security breaches. Until today, researchers face the problem of building reliable and effective IDSs that can handle numerous attacks with changing patterns. This paper deals with feed-forward neural network (FNN) training problems using the application of a recently invented meta-heuristic optimization algorithm locust swarm optimization (LSO) for the first time. FNN is combined with LSO (FNN-LSO) to build an advanced detection system and improve the performance of IDS. Our method is applied to a series of experiments to study the capability and performance of the proposed approach. Experimental studies began by using intrusion detection evaluation data, namely, NSL-KDD and UNSW-NB15, to benchmark the performance of the proposed approach. The most common evolutionary trainers, namely, particle swarm optimizer PSO-based trainer and genetic algorithm GA-based trainer, were implemented to verify the results. Compared with existing methods in the literature, our proposed approach provides to be more accurate to be an alternative solution for IDS. The experimental results show that our training algorithm not only attained a very good performance in terms of speed convergence but also achieved reliability due to the reduced likelihood of being trapped in local minima. Furthermore, our proposed model improves the detection rate.