Semisupervised-Learning-Based Security to Detect and Mitigate Intrusions in IoT Network

• Published in: IEEE internet of things journal 2020-11, Vol.7 (11), p.11041-11052
• Main Authors: Ravi, Nagarathna, Shalinie, S. Mercy
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Cloud computing; Clustering; Computer architecture; Data deluge (DD) attack; Datasets; fog computing; Internet of Things; Internet of Things (IoT); intrusion; Intrusion detection systems; Machine learning; Model testing; Performance degradation; Security; Semi-supervised learning; semisupervised learning; Switches; Training
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2020.2993410

Our world is moving toward an Internet of Things (IoT) era by connecting billions of IoT. There are several security loopholes in the IoT network. Intrusion can lead to performance degradation and pose a threat to data security. Hence, there is a need for a method to detect intrusion in the IoT networks. Existing solutions use supervised-learning-based intrusion detection methods that need a huge labeled data set for better accuracy. It is not easy to source out a huge labeled data set because the size of the IoT network is huge. To overcome some of the impediments in the existing solutions, we propose a novel SDRK machine learning (ML) algorithm to detect intrusion. SDRK leverages supervised deep neural networks (DNNs) and unsupervised clustering techniques. The intrusion detection and mitigation algorithms are placed in the fog nodes that are between IoT and cloud layers. We test our proposed methodology against the data deluge (DD) attack in the testbed. The SDRK model is tested on the benchmark NSL-KDD data set. We compare the results with state-of-the-art solutions. When testing with the NSL-KDD data set, we find that SDRK detects the attacks with improved accuracy of 99.78%.