Passive Rule-Based Approach to Detect Sinkhole Attack in RPL-Based Internet of Things Networks

• Published in: IEEE access 2023-01, Vol.11, p.1-1
• Main Authors: Al-Sarawi, Shadi, Anbar, Mohammed, Alabsi, Basim Ahmad, Aladaileh, Mohammad Adnan, Rihan, Shaza Dawood Ahmed
• Format: Article
• Language: English
• Subjects: Behavioral sciences; Data collection; Distributed Denial of Service (DDoS); International Data Corporation (IDC); Internet of Things; Internet of Things (IoT); Internet Protocol Version 6 (IPv6); IP (Internet Protocol); IP networks; IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN); Monitoring; Nodes; Personal area networks; Power consumption; Power demand; Power management; Routing; Routing (telecommunications); Routing Protocol for Low-Power and Lossy Networks (RPL); Routing protocols; Sinkholes; Wireless networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3310242

An Internet of Things (IoT) refers to a network of smart devices that enable data collection and exchange. RPL is a protocol specifically designed for IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN) to bring the concept of IoT into reality. As a result, RPL has become a standard routing protocol for connecting IPv6 to IoT networks. However, like any other network protocol, RPL is vulnerable to various attacks, including sinkhole attacks, which can disrupt network operations. Sinkhole attacks exploit vulnerabilities in RPL by manipulating routing preferences by disseminating falsified data, leading to an abnormal increase in traffic directed toward the attacker's node. This paper introduces the Passive Rule-based Approach (PRBA) to detect sinkhole nodes in RPL-based IoT networks. The PRBA approach relies on three proposed behavioral indicators: (I) Bi-Directional behavior, (II) Bi-Directional Frequently behavior, and (III) Power Consumption behavior. The proposed PRBA approach was implemented and evaluated using the COOJA simulator and compared with state-of-the-art approaches. Simulation results demonstrate that the PRBA approach achieves a detection accuracy rate ranging from 90% to 100%, with a false-positive rate ranging from 0% to 0.2%. Additionally, due to its carefully designed deployment strategy, the proposed approach satisfies the power consumption requirements of constrained nodes without causing an increase in power consumption.