Modeling of Intrusion Detection System Using Double Adaptive Weighting Arithmetic Optimization Algorithm with Deep Learning on Internet of Things Environment

The Internet of Things (IoT) has experienced rapid development in area-specific applications,
including smart transportation systems, healthcare, industries, and smart agriculture, to enhance socioeconomic development over the past few years. This IoT system includes different actuators, interconnected
sensors, and network-enabled devices that exchange various data through private networks and the Internet
infrastructure. The intrusion detection system (IDS) is deployed with preventive security mechanisms, namely
access control and authentication. The usual behaviors of the mechanism distinguish malicious and normal
activities based on specific patterns or rules of IDSs. Therefore, this article focuses on developing IDS using
Double Adaptive Weighting Arithmetic Optimization Algorithm with Deep Learning (DAWAOA-DL) approach
in the IoT environment. The DAWAOA-DL methodology's objective is to recognise and classify intrusions in
the IoT platform accurately. To execute this, the presented DAWAOA-DL approach involves the design of
the DAWAOA technique for the feature selection procedure. Next, the convolutional neural network-gated
recurrent unit (CNN-GRU) technique is used for the intrusion detection task. Finally, the Adam optimizer is
exploited as a hyperparameter optimizer of the CNN-GRU methodology. A series of simulations were
performed on the BoT-IoT dataset to exhibit the effectual detection performance of the DAWAOA-DL method.
A widespread experimental validation demonstrated the betterment of the DAWAOA-DL method over other
recent models under several metrics.