Research Area: Machine Learning
Internet of things (IoT) has become an emerging technology transforming everyday physical objects to be smarter by using underlying technologies such as sensor networks. Routing protocol for low power and lossy network (RPL) is considered one of the promising protocols designed for IoT networks. However, due to the constrained nature of the IoT devices in terms of memory, processing power, and network capabilities, they are exposed to many security attacks. Unfortunately, the existing Intrusion Detection System (IDS) approaches using machine learning that has been proposed to detect and mitigate security attacks in internet networks are not suitable for analyzing IoT traffics. This paper proposed an IDS system using the hybridization of supervised and semi-supervised deep learning for network traffic classification of known and unknown abnormal behaviors in the IoT environment. In addition, we have developed a new IoT specialized dataset named IoTR-DS, using the RPL protocol. IoTR-DS is used as a use case to classify three known security attacks (DIS, Rank, and Wormhole). The proposed Hybrid DL-Based IDS is evaluated and compared to some existing ones, and the results are promising. The evaluation results show an accuracy-detecting rate of 98% and 93% f1-score of multi-class attacks when using pre-trained attacks (known) and an average accuracy of 95% and 87% f1-score when predicting untrained two attack behaviors
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Author(s) Name: Yahya Al Sawafi,Abderezak Touzene,Rachid Hedjam
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Publisher name: Sultan Qaboos University
DOI: 10.2139/ssrn.3994183
Volume Information: 38 Pages
