Genetic Search Wrapper-Based Naïve Bayes Anomaly Detection Model for Fog Computing Environment

Abstract

Fog computing will provide low-latency connectivity between smartphone devices and the cloud as a complement to cloud computing. Fog devices can, however, face security related challenges as fog nodes are near to end users with restricted computing capabilities. Traditional network attacks break the fog node system. While the intrusion detection system (IDS) has been well studied in traditional networks, it may sadly be impractical to use it specifically in the fog environment. Fog nodes still produce large quantities of data and thus allowing the IDS in the fog context over big data is of the utmost importance. In order to counter some of these network attacks, a proactive security defense technology, Intrusion Detection System (IDS), can be used in the fog environment using data mining technique for network anomaly detection and network event classification attack has proven efficient and accurate. This research presents a Genetic Search Wrapper-based Naïve Bayes anomaly detection model (GSWNB) in Fog Computing environment that eliminates extraneous features to minimise time complexity as well as building an improved model that predict result with a higher accuracy using NSL-KDD dataset as benchmark dataset. From the experiment, the proposed model demonstrates a higher overall performance of 99.73% accuracy, keeping the false positive rate as low as 0.006.