Saba, T, Rehman, A, Sadad, T, Kolivand, H and Bahaj, SA (2022) Anomaly-based intrusion detection system for IoT networks through deep learning model. Computers and Electrical Engineering, 99. ISSN 0045-7906

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Abstract

The Internet of Things (IoT) idea has been developed to enhance people's lives by delivering a diverse range of smart interconnected devices and applications in several domains. However, security threats are main critical challenges for the devices in an IoT environment. Many approaches have been proposed to secure IoT appliances in state of the art, still advancement is desirable. Machine learning has demonstrated a capability to detect patterns when other methodologies have collapsed. One advanced method to enhance IoT security is to employ deep learning. This formulates a seamless option for anomaly-based detection. This paper presents a CNN-based approach for anomaly-based intrusion detection systems (IDS) that takes advantage of IoT's power, providing qualities to efficiently examine whole traffic across the IoT. The proposed model shows ability to detect any possible intrusion and abnormal traffic behavior. The model is trained and tested using the NID Dataset and BoT-IoT datasets and achieved an accuracy of 99.51% and 92.85%, respectively.

Item Type:	Article
Uncontrolled Keywords:	0803 Computer Software, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Computer Science & Mathematics
Publisher:	Elsevier
Date Deposited:	04 Mar 2022 10:37
Last Modified:	17 Feb 2023 00:50
DOI or ID number:	10.1016/j.compeleceng.2022.107810
URI:	https://researchonline.ljmu.ac.uk/id/eprint/16451

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