Rank and wormhole attack detection model for RPL-based Internet of Things using machine learning

The proliferation of the internet of things (IoT) technology has led to numerous challenges in various life domains, such as healthcare, smart systems, and mission-critical applications. The most critical issue is the security of IoT nodes, networks, and infrastructures. IoT uses the routing protocol for low-power and lossy networks (RPL) for data communication among the devices. RPL comprises a lightweight core and thus does not support high computation and resource-consuming methods for security implementation. Therefore, both IoT and RPL are vulnerable to security attacks, which are broadly categorized into RPL-specific and sensor-network-inherited attacks. Among the most concerning protocol-specific attacks are rank attacks and wormhole attacks in sensor-network-inherited attack types. They target the RPL resources and components including control messages, repair mechanisms, routing topologies, and sensor network resources by consuming. This leads to the collapse of IoT infrastructure. In this paper, a lightweight multiclass classification-based RPL-specific and sensor-network-inherited attack detection model called MC-MLGBM is proposed. A novel dataset was generated through the construction of various network models to address the unavailability of the required dataset, optimal feature selection to improve model performance, and a light gradient boosting machine-based algorithm optimized for a multiclass classification-based attack detection. The results of extensive experiments are demonstrated through several metrics including confusion matrix, accuracy, precision, and recall. For further performance evaluation and to remove any bias, the multiclass-specific metrics were also used to evaluate the model, including cross-entropy, Cohn’s kappa, and Matthews correlation coefficient, and then compared with benchmark research

A Trust-Based Intrusion Detection System for RPL Networks: Detecting a Combination of Rank and Blackhole Attacks

Routing attacks are a major security issue for Internet of Things (IoT) networks utilising routing protocols, as malicious actors can overwhelm resource-constrained devices with denial-of-service (DoS) attacks, notably rank and blackhole attacks. In this work, we study the impact of the combination of rank and blackhole attacks in the IPv6 routing protocol for low-power and lossy (RPL) networks, and we propose a new security framework for RPL-based IoT networks (SRF-IoT). The framework includes a trust-based mechanism that detects and isolates malicious attackers with the help of an external intrusion detection system (IDS). Both SRF-IoT and IDS are implemented in the Contiki-NG operating system. Evaluation of the proposed framework is based on simulations using the Whitefield framework that combines both the Contiki-NG and the NS-3 simulator. Analysis of the simulations of the scenarios under active attacks showed the effectiveness of deploying SRF-IoT with 92.8% packet delivery ratio (PDR), a five-fold reduction in the number of packets dropped, and a three-fold decrease in the number of parent switches in comparison with the scenario without SRF-IoT. Moreover, the packet overhead introduced by SRF-IoT in attack scenarios is minimal at less than 2%. Obtained results suggest that the SRF-IoT framework is an efficient and promising solution that combines trust-based and IDS-based approaches to protect IoT networks against routing attacks. In addition, our solution works by deploying a watchdog mechanism on detector nodes only, leaving unaffected the operation of existing smart devices