Dr Sana Ullah Jan S.Jan@napier.ac.uk
Lecturer
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.
Citation
Jan, S. U., Ahmed, S., Shakhov, V., & Koo, I. (2019). Toward a Lightweight Intrusion Detection System for the Internet of Things. IEEE Access, 7, 42450-42471. https://doi.org/10.1109/access.2019.2907965
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 | Mar 29, 2024 |
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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