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Title	Routing Attack Induced Anomaly Detection In Iot Network Using Rbm-Lstm
ID_Doc	47090
Authors	Sahay R.; Nayyar A.; Shrivastava R.K.; Bilal M.; Singh S.P.; Pack S.
Year	2024
Published	ICT Express, 10, 3
DOI	http://dx.doi.org/10.1016/j.icte.2024.04.012
Abstract	The network of resource constraint devices, also known as the Low power and Lossy Networks (LLNs), constitutes the edge tire of the Internet of Things applications like smart homes, smart cities, and connected vehicles. The IPv6 Routing Protocol over Low power and lossy networks (RPL) ensures efficient routing in the edge tire of the IoT environment. However, RPL has inherent vulnerabilities that allow malicious insider entities to instigate several security attacks in the IoT network. As a result, the IoT networks suffer from resource depletion, performance degradation, and traffic disruption. Recent literature discusses several machine learning algorithms to detect one or more routing attacks. However, IoT infrastructures are expanding, and so are the attack surfaces. Therefore, it is essential to have a solution that can adapt to this change. This paper introduces a comprehensive framework to detect routing attacks within Low Power and Lossy Networks (LLNs). The proposed solution leverages deep learning by combining Restricted Boltzmann Machine (RBM) and Long Short-Term Memory (LSTM). The framework is trained on 11 network parameters to understand and predict normal network behavior. Anomalies, identified as deviations from the forecast trends, serve as indicators of potential routing attacks and thus address vulnerabilities in the RPL. © 2024 The Author(s)
Author Keywords	Anomaly detection; Internet of Things; LSTM; RBM; Routing attacks; RPL

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