A new method to detect attacks on the Internet of Things (IoT) using adaptive learning based on cellular learning automata. Issue 1 (21st December 2021)

• Record Type: Journal Article
• Authors: Dogani, Javad; Farahmand, Mahdieh; Daryanavard, Hassan
• Is Part Of: ETRI journal. Volume 44:Issue 1(2022)
• Page Start: 155
• Page End: 167
• Publication Date: 2021-12-21
• Subjects: adaptive learning -- cellular learning automata (CLA) -- energy consumption -- Internet of Things (IoT) -- network lifetime -- reliability -- security; Telecommunication -- Periodicals; Electronics -- Periodicals; Electronics; Telecommunication; Periodicals; Periodicals; 621.38205
• DOI: 10.4218/etrij.2021-0044
• Languages: English
• ISSNs: 1225-6463

Abstract

Abstract: The Internet of Things (IoT) is a new paradigm that connects physical and virtual objects from various domains such as home automation, industrial processes, human health, and monitoring. IoT sensors receive information from their environment and forward it to their neighboring nodes. However, the large amounts of exchanged data are vulnerable to attacks that reduce the network performance. Most of the previous security methods for IoT have neglected the energy consumption of IoT, thereby affecting the performance and reducing the network lifetime. This paper presents a new multistep routing protocol based on cellular learning automata. The network lifetime is improved by a performance‐based adaptive reward and fine parameters. Nodes can vote on the reliability of their neighbors, achieving network reliability and a reasonable level of security. Overall, the proposed method balances the security and reliability with the energy consumption of the network.