An Enhanced Exploration and Exploitation of Modified Grey Wolf Optimizer for Fuzzy Rules Reduction in Cloud Intrusion Detection System (CIDS)

Optimization problems in the Cloud Intrusion Detection System (CIDS) contain numerous conflicting objectives, unimodal and multi-modal functions and the difficulty level varying between linear to non-linear limits. The Grey Wolf Optimizer (GWO) is a meta-heuristic technique that is implemented based on social behavior of wolves and their hunting behavior. Significant improvement in exploration and exploitation of the search space in GWO can be obtained by modifying the control parameter a. Works have already been carried out by many researchers by modifying the control parameter a in different manner to achieve better results. In a similar content, the authors of this study also modified the control parameter a and added a weight factor to the position of each wolf to attain the best possible results. Due to high demand for the cloud computing environment, intrusion detection in a cloud network plays a big role in maintaining the faith of the clients. Hence, CIDS is required to inspect the network packets to identify the abnormal behavior. For developing a system for cloud based IDS, the researchers created fuzzy rules to represent the relationship between the attributes and the nature of activity (normal or abnormal). The Modified Grey Wolf Optimizer (MGWO) algorithm is applied on eleven benchmark test functions and obtained good performance metrics. The results presented in this paper are promising; MGWO is used to reduce the fuzzy rules in developing fuzzy based CIDS. The performance of the proposed algorithm is compared with classical GWO, Particle Swarm Optimization (PSO), Cuckoo Search (CS) and variant of MGWO. The experimental results reveal that there is significant improvement in its performance