Abstract
The Internet of Things (IoT) has experienced rapid development in area-specific applications, including smart transportation systems, healthcare, industries, and smart agriculture, to enhance socio-economic 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.
Keywords:
Internet of Things; Security; Hybrid deep learning; Intrusion detection; Feature selection
HIGHLIGHTS
Developing IDS DAWAOA-DL approach in the IoT environment.
CNN-GRU technique is used for the intrusion detection task.
A series of simulations were performed on the BoT-IoT dataset.
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