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Toward a Lightweight Intrusion Detection System for the Internet of Things

Jan, Sana Ullah; Ahmed, Saeed; Shakhov, Vladimir; Koo, Insoo

Authors

Saeed Ahmed

Vladimir Shakhov

Insoo Koo



Abstract

Integration of the Internet into the entities of the different domains of human society (such as smart homes, health care, smart grids, manufacturing processes, product supply chains, and environmental monitoring) is emerging as a new paradigm called the Internet of Things (IoT). However, the ubiquitous and wide-range IoT networks make them prone to cyberattacks. One of the main types of attack is a denial of service (DoS), where the attacker floods the network with a large volume of data to prevent nodes from using the services. An intrusion detection mechanism is considered a chief source of protection for information and communications technology. However, conventional intrusion detection methods need to be modified and improved for application to the IoT owing to certain limitations, such as resource-constrained devices, the limited memory and battery capacity of nodes, and specific protocol stacks. In this paper, we develop a lightweight attack detection strategy utilizing a supervised machine learning-based support vector machine (SVM) to detect an adversary attempting to inject unnecessary data into the IoT network. The simulation results show that the proposed SVM-based classifier, aided by a combination of two or three incomplex features, can perform satisfactorily in terms of classification accuracy and detection time.

Journal Article Type Article
Acceptance Date Mar 24, 2019
Online Publication Date Mar 28, 2019
Publication Date 2019
Deposit Date Oct 21, 2022
Publicly Available Date Oct 21, 2022
Journal IEEE Access
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 7
Pages 42450-42471
DOI https://doi.org/10.1109/access.2019.2907965
Keywords Intrusion detection system, anomaly detection, Internet of Things, support vector machine
Public URL http://researchrepository.napier.ac.uk/Output/2937019

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