Oppositional Coyote Optimization based Feature Selection with Deep Learning Model for Intrusion Detection in Fog-Assisted Wireless Sensor Network

Recently, Wireless Sensor Networks (WSN) and the Internet of Things (IoT) become widespread in several real-time applications. Since IoT devices have generated a huge amount of data, the processing of data at the cloud server leads to high delay. To reduce the delay, fog-assisted WSN can be developed where the Fog Nodes are kept at the edge of the network nearer to the client. Besides, security becomes a challenging issue in fog-assisted WSN and can be accomplished by using Intrusion Detection System (IDS). This paper presents an Oppositional Coyote Optimization based feature selection with Cat Swarm Optimization based Bidirectional Gated Recurrent Unit (OCOA-CSBiGRU) for intrusion detection in fog-assisted WSN. The intention of the OCOA-CSBiGRU technique is to identify the occurrence of intrusions in the fog-assisted WSN by the use of feature selection and classification models. The proposed OCOA-CSBiGRU technique initially designs a novel OCOA-based feature selection technique for the optimal selection of features. Besides, the BiGRU model is utilized for the detection and classification of intrusions. In order to improve the detection efficiency of the BiGRU model, the Cat Swarm Optimization (CSO) algorithm has been utilized. A comprehensive experimental analysis is carried out on benchmark datasets, and the results indicatebetter outcomes of the OCOA-CSBiGRU technique over the recent methods interms of different metrics.