Deep Learning and Elephant Herd Based IOT Network Intrusion Alarming System
Year: 2024
Type: article
Abstract: —The Internet of Things (IoT) have various applications in different sectors of with each passing day. The rapid development of IoT and its increasing demand in different fields of life create a serious problem of security for the IoT environment, which needs serious consideration to protect the IoT-enabled systems from external networks and cyber-attacks.IDS solutions are now more essential than ever because of the rising use of Internet of Things (IoT) devices. This paper proposes a deployment-ready network IDS for IoT that overcomes the shortcomings of the existing IDS solutions. This paper has proposed a model for IOT network intrusion detection using optimized features. In order to optimize data Elephant Herd optimization algorithm was developed. Filtered features were further process by convolutional and maxpooing operator to train the deep neural network. Experiment was done on real dataset and result shows that proposed DEIIDS (Deep Learning & Elephant based IOT Intrusion Detection System) model successfully detect intrusion and its classes.
Authors: Chaudhary, Diwakar Kumar, Yadav, Pritaj, Jha, Kanchan
Institution: AISECT University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6967950045
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