MCTE-RPL: A multi-context trust-based efficient RPL for IoT

The Internet of things (IoT) is highly exposed to various attacks due to its sensitive applications, but it is very vulnerable in dealing with these attacks. So, various studies have been introduced to improve IoT security. Most of methods have focused on improving the security of the RPL protocol (...

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Veröffentlicht in:Journal of network and computer applications 2024-09, Vol.229, p.103937, Article 103937
Hauptverfasser: Mohajerani, Javad, Mohammadi Ghanatghestani, Mokhtar, Hashemipour, Malihe
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Sprache:eng
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Zusammenfassung:The Internet of things (IoT) is highly exposed to various attacks due to its sensitive applications, but it is very vulnerable in dealing with these attacks. So, various studies have been introduced to improve IoT security. Most of methods have focused on improving the security of the RPL protocol (Low-Power and Lossy Networks routing protocol) based on the development of trust models. However, most of these researches have considered good and bad behaviors to calculate the value of trust. This way of evaluating trust alone is not enough and it is vulnerable in dealing with some attacks, especially smart attacks (such as on-off attack, selective sending). To improve this issue, in this paper, a method called multi-context trust-based efficient RPL for IoT (MCTE-RPL) is proposed based on RPL development utilizing trust models with intrusion detection system. MCTE-RPL focuses on three important principles, including establishing secure and reliable routing topology, evaluating trust, and detecting malicious nodes. In the first step, the network routing topology is formed based on the trust and conditions of the nodes. In the second step, in accordance with the data exchanges, the trust of the nodes is evaluated and malicious factors are identified. The simulation results using cooja indicated the superiority of MCTE-RPL in improving routing reliability and data exchange compared to previous studies.
ISSN:1084-8045
DOI:10.1016/j.jnca.2024.103937