期刊
IEEE ACCESS
卷 11, 期 -, 页码 71073-71087出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2023.3246180
关键词
IoT; machine learning; network layer; RPL; sinkhole attack
Internet of Things (IoT) has transformed the world with millions of connected devices. However, with the growth of IoT, ensuring IoT security is crucial. The routing protocol for low power and lossy networks (RPL) is specifically designed for routing in IoT devices. This study focuses on internal attacks on the network layer, particularly the detection and prevention of sinkhole attacks. The review includes state-of-the-art solutions and performance parameters, as well as an analysis of machine learning algorithms for securing the RPL protocol against internal attacks.
Internet of Things (IoT) has revolutionized the world in the last decade. Today millions of devices are connected to each other utilizing IoT technology in one way or the other. With the significant growth in IoT devices, the provision of IoT security is imperative. Routing protocol for low power and lossy networks (RPL) is a network layer protocol, specially designed for routing in IoT devices. RPL protocol faces many attacks such as selective forwarding attacks, blackhole attacks, sybil attacks, wormhole attacks, and sinkhole attacks. All these attacks pose great threats to IoT networks and can substantially affect the performance of the network. In this work, a comprehensive review of internal attacks on the network layer is presented. Specifically, we focus on the literature that considers presenting solutions for the detection and prevention of sinkhole attacks. We reviewed the state-of-the-art works and different performance parameters like energy consumption, scalability, threshold value, packet delivery ratio, and throughput. Moreover, we also present a detailed analysis of machine learning-based algorithms and techniques proposed for the security of RPL protocol against internal attacks.
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