Antlion Optimization-Based Feature Selection Scheme for Cloud Intrusion Detection Using Naïve Bayes Algorithm

Abstract

The popularity of cloud computing is due to its countless benefits which include flexibility, scalability, and cost effectiveness. This refers to the availability of services and computing resources on demand to users with little management drive via internet technology. One of the major challenges faced by this technol ogy is the issue of security which is making both service providers and users to worry about the safety of cloud resources. It is on this note that Cloud Intrusion Detection System (CIDS) is mostly deployed into cloud environment to identify and also prevent attacks in some instance. In this research work, a cloud intrusion detection system that identifies malicious activities inside cloud, utilizing Ant Lion Optimization (ALO) algorithm for feature selection and Bayesian Classifier was developed. Experimental result shows 96.22% accuracy, 0.379% FPR, 96.16% (Recall, Precision and F-Measure), and 92.36% Kappa Statistics.