# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
###### Abstract  
For networks with limited resources, such as IoT-enabled smart homes, smart industrial equipment, and urban infrastructures, the Routing Protocol for Low-power and Lossy Networks (RPL) was developed. Additionally, a number of optimizations have been suggested for its application in other contexts, such as smart hospitals, etc. Although these networks offer efficient routing, the lack of active security features in RPL makes them vulnerable to attacks. The types of attacks include protocol-specific ones and those inherited by wireless sensor networks. They have been addressed by a number of different proposals, many of which have achieved substantial prominence. However, concurrent handling of both types of attacks is not considered while developing a machine-learning-based attack detection model. Therefore, the ProSenAD model is proposed for addressing the identified gap. Multiclass classification has been used to optimize the light gradient boosting machine model for the detection of protocol-specific rank attacks and sensor network-inherited wormhole attacks. The proposed model is evaluated in two different scenarios considering the number of attacks and the benchmarks for comparison in each scenario. The evaluation results demonstrate that the proposed model outperforms with respect to the metrics including accuracy, precision, recall, Cohen's Kappa, cross entropy, and the Matthews correlation coefficient. RPL protocol; secure IoT; protocol-specific attacks; sensor network-inherited attacks; attack detection; machine learning +
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At the base of IoT architectures, Wireless Sensor Networks (WSNs) are situated in which the nodes are resource constrained. Conventionally, they depend on power-scavenging technologies to communicate and function [6]. Therefore, these domains are under continuous research for enhancements concerning the development of compatible components for deployment in resource-constrained environments [7]. It is typical for such systems to have inadequate storage capacity as well as low processing power. These characteristics require befitting communication protocols and network standards for data transmission. Figure 1 illustrates a use case of an IoT-enabled smart healthcare system. IoT systems use RPL for routing and run on 6 LoWPAN [9]. However, the low-power and lossy characteristics, heterogeneity, and resource-constrained nature make them highly vulnerable to security attacks. These attacks are classified into two main categories: (1) protocol-specific and (2) Sensor Network (SN)-inherited attacks [10]. It is necessary to investigate these attacks and propose adequate solutions for a secure transition toward IoT technology on a large scale. In the literature, various approaches for addressing the routing and network security in IoT are proposed, including machine-learning-based (ML-based), intrusion-detection-based, and protocol-optimization-based strategies [11]. Because of the various advantages such as automation, feature extraction, and pattern recognition in the network traffic data, the ML-based approach has the potential to fulfill the routing security needs. Furthermore, because IoT produces big data, it highly benefits the ML-based model building procedures because of their dependence on the datasets. Several ML-based solutions have been proposed by researchers to address the security attacks in IoT. However, in the RPL-based IoT domain, the concurrent detection of protocol-specific and SN-inherited attacks is insufficiently addressed, particularly for protocol-specific rank (PS-R) attacks and SN-inherited wormhole (SN-W) attacks. These attacks are among the most detrimental and damaging attacks for the routing mechanisms and network resources. This research work focuses on the security of IoT networks that implement RPL for routing. An ML-based model is proposed to improve the security against PS-R attacks as well as SN-W attacks.