Intrusion detection using optimized ensemble classification in fog computing paradigm

Abstract

Fog computing is a framework, which expands services of the cloud to network for addressing inherent issues of the cloud. Intrusion Detection Systems (IDSs) represent a major unit of the security model for fog networks for facilitating service quality. This paper devises a hybrid optimization-driven ensemble classifier in a fog computing platform. In fog computing, the complete processing is done utilizing trio layers that include the cloud layer, end point layer, and fog layer. In the cloud layer, three procedures, like data transformation, selection of features, and classification processes were carried out. A data transformation is conducted with log transformation. A feature is chosen using Kolmogorov–Smirnov correlation-based filter. Then, the classification is done utilizing ensemble classifiers, named RideNN, Deep Neuro-Fuzzy Network (DNFN), and Shepard convolutional neural network (ShCNN). The tuning of the ensemble classifier is conducted utilizing the developed Rider Sea Lion Optimization (RSLO) algorithm. RSLO algorithm is conceived by amalgamating the Rider optimization algorithm (ROA) and Sea Lion Optimization Algorithm (SLnOA). At an end point layer, a physical process is done. In the fog layer, intrusion detection is done based on a trained ensemble classifier. The proposed RSLO-based ensemble approach provides enhanced performance with higher precision of 88%, recall of 88.4%, F-measure of 88.2%, and accuracy of 97.2%.