Volume 16, Issue 4 (12-2024)                   itrc 2024, 16(4): 20-32 | Back to browse issues page

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Nataj Solhdar M H, Erfani Majd N. The combination of machine learning methods for intrusion detection system in the Internet of Things (IoT). itrc 2024; 16 (4) :20-32
URL: http://journal.itrc.ac.ir/article-1-642-en.html
The combination of machine learning methods for intrusion detection system in the Internet of Things (IoT)
Mohammad Hassan Nataj Solhdar1 , Nasser Erfani Majd 2
1- Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz Ahvaz, Iran
2- Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz Ahvaz, Iran , n.erfanimajd@scu.ac.ir
Abstract:   (215 Views)
Due to the large scale of the IoT, cloud computing capabilities such as data storage, management, and analysis are close to the edge of fog network. As the internet becomes more widely used in our business operations through the IoT, the desire for secure and efficient communication also increases. Fog and cloud security is an issue associated with any pattern of data storage, management or processing of data. If a network attack occurs, it has irreversible and destructive effects on the development of the IoT, fog, and cloud computing. Therefore, many security systems or models have been proposed or implemented for fog security reasons. Intrusion detection systems are one of the best options designed using artificial intelligence. In this paper, we present an intrusion detection system for fog security against cyber-attacks. The proposed model uses several machine learning methods designed for the security of fog computing and IoT devices. We used the comprehensive NSLKDD standard dataset for our proposed model. The performance of our model is measured using a variety of common metrics and compared with other methods.
Keywords: IoT, intrusion detection, classification
Full-Text [PDF 825 kb]   (133 Downloads)    
Type of Study: Research | Subject: Information Technology
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