Improved adaptive neuro-fuzzy inference system based on modified salp swarm algorithm and golden eagle optimizer algorithm for intrusion detection in networks

With the increase in the growth of computer networks throughout the past years, network security has become an essential issue. Among the numerous network security measures, intrusion detection systems play a dynamic function with integrity, confidentiality, and accessibility of resources. An Intrusion Detection System (IDS) is a software program or hardware device which monitors computer system and/or network activities for malicious activities and produces alerts to security experts. In IDS there are three major problems namely generating many alerts, a huge rate of false positive alerts, and unknown attack types per generated alerts. Alert management methods are used to manage these problems. One of the methods of alert management is alert reduction and alert classification. The proposed approach focuses on enhancing the efficiency of the adaptive neuro-fuzzy inference system (ANFIS) using a modified salp swarm algorithm (SSA) and Golden Eagle optimizer (GEOSSA). The present study uses the Golden Eagle optimization algorithm to improve SSA behaviors. The proposed model (GEO-SSA-ANFIS) intends to determine the appropriate parameters using the GEO-SSA algorithm because these parameters are considered the main component affecting the ANFIS forecasting process. The results of the intrusion detection based on the NSL-KDD dataset were better and more efficient compared with those models because the detection rate was 96.68% and the FAR result was 0.438%.