Intrusion Detection in Networks using Honey Badger Algorithm with improved Adaptive Neuro-Fuzzy Inference System

An Intrusion detection system IDS is a key component of the security management infrastructure. The goal of IDS is to monitor the processes prevailing in a network and to analyze them for signs of any possible deviations. Because of the large volume of data, the network gets expansion with a false alarm rate of intrusion, and detection accuracy decreased. It is one of the main problems in unknown attacks on network experiences. The basic aim was to develop accuracy also reduce the false alarm rate (FAR). We will design an Adaptive Neuro-Fuzzy based system to identify the intrusion activities within a network effectively. The Honey Badger Algorithm with an improved Adaptive Neuro-Fuzzy Inference System (HBA-IANFIS) is used to address the problems presented. The IANFIS combines an artificial neural network and a fuzzy interference system. To enhance the performance of the IANFIS model, the Honey Badger Algorithm is used to optimize the IANFIS. The intrusion detection results based on the NSL-KDD dataset were better and more efficient than those models because the detection rate was 96.68%, and the FAR result was 0.438%.