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

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 fogassisted 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