Smart defense against distributed Denial of service attack in IoT networks using supervised learning classifiers. (March 2022)

• Record Type: Journal Article
• Authors: Gupta, B.B.; Chaudhary, Pooja; Chang, Xiaojun; Nedjah, Nadia
• Is Part Of: Computers & electrical engineering. Volume 98(2022)
• Publication Date: 2022-03
• Subjects: Internet of things (IoT) networks -- Distributed Denial of Service (DDoS) attack -- Consumer IoT (CIoT) devices -- Machine learning algorithms -- Botnet -- IoT security; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107726
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Design an approach to detect DDoS attack in consumer IoT networks. Prepare a dataset containing relevant IoT network features for enhanced attack detection. Attack is detected using prominent Machine Learning classifiers. Performance assessment is completed on proposed dataset to illuminate its detection efficacy. Abstract: From smart home to industrial automation to smart power grid, IoT- based solutions penetrate into every working field. These devices expand the attack surface and turned out to be an easy target for the attacker as resource constraint nature hinders the integration of heavy security solutions. Because IoT devices are less secured and operate mostly in unattended scenario, they perfectly justify the requirements of attacker to form botnet army to trigger Denial of Service attack on massive scale. Therefore, this paper presents a Machine Learning-based attack detection approach to identify the attack traffic in Consumer IoT (CIoT). This approach operates on local IoT network-specific attributes to empower low-cost machine learning classifiers to detect attack, at the local router. The experimental outcomes unveiled that the proposed approach achieved the highest accuracy of 0.99 which confirms that it is robust and reliable in IoT networks. Graphical abstract: Image, graphical abstract