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An efficient Genetic algorithm-based defensive method to mitigate multiple attacks in RPL-enabled IoT network
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AI
DOI：10.1016/j.iot.2025.101614.png)
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Abstract
En 中文
Internet of Things (IoT) is the interconnection of billions of devices over the Internet. It is an umbrella of various concepts, protocols, and technologies that are used to create numerous benefits in productivity and automation. Despite the benefits it provides, there are challenges such as high cost of IoT devices, time-constraints, and overuse of Internet protocols and technologies which attackers often take advantage. To address this, IoT networks must have secure routing protocols that can provide security to the network and also utilize the benefits of existing technologies. However the lack of infrastructure, dynamic topology changes, resource constraints, and unreliable links make even the best existing protocol Routing Protocol for Low Power and Lossy Network (RPL) to be vulnerable for various attacks. Besides the trust management, that ensures only the reliable and legitimate nodes to participate in routing decisions, is another critical aspect that many existing solutions fail to consider. Hence, in this research, we propose a novel secure routing technique Genetic Algorithm-based Trusted framEwork for RPL (GATE-RPL) that supports multi-topology routing and provides security to various devices in the IoT network. To overcome the security issues, the proposed work:(i) provides a dynamic trust management technique that maximizes the trust of nodes, links, and routing performance using a combination of K-means clustering and extended Genetic algorithm; and subsequently (ii) finds a trusted routing path between every node in the network. The experimental results indicate an average of 0.012% packet loss, 10.5 Mbps throughput, and 99% accuracy in identifying trustworthy routing paths.
Keywords:
IoT
RPL
Genetic algorithm
K-means clustering algorithm
Trust calculation