Security analysis of network anomalies mitigation schemes in IoT networks

Lawal, Muhammad Aminu, Shaikh, Riaz Ahmed and Hassan, Syed Raheel (2020) Security analysis of network anomalies mitigation schemes in IoT networks. IEEE Access, 8. pp. 43355-43374. ISSN 2169-3536

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Abstract

The Internet of Things (IoT) is on the rise and it is giving a new shape to several fields such as smart cities, smart homes, smart health, etc. as it facilitates the connection of physical objects to the internet. However, this advancement comes along with new challenges in terms of security of the devices in the IoT networks. Some of these challenges come as network anomalies. Hence, this has prompted the use of network anomaly mitigation schemes as an integral part of the defense mechanisms of IoT networks in order to protect the devices from malicious users. Thus, several schemes have been proposed to mitigate network anomalies. This paper covers a review of different network anomaly mitigation schemes in IoT networks. The schemes' objectives, operational procedures, and strengths are discussed. A comparison table of the reviewed schemes, as well as a taxonomy based on the detection methodology, is provided. In contrast to other surveys that presented qualitative evaluations, our survey provides both qualitative and quantitative evaluations. The UNSW-NB15 dataset was used to conduct a performance evaluation of some classification algorithms used for network anomaly mitigation schemes in IoT. Finally, challenges and open issues in the development of network anomaly mitigation schemes in IoT are discussed.

Item Type: Article
Uncontrolled Keywords: classification algorithms,internet of things (iot),intrusion detection system (ids),machine learning,network anomalies,security,computer science(all),materials science(all),engineering(all) ,/dk/atira/pure/subjectarea/asjc/1700
Faculty \ School: Faculty of Science > School of Computing Sciences
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Depositing User: LivePure Connector
Date Deposited: 30 May 2022 13:30
Last Modified: 05 Jul 2022 00:19
URI: https://ueaeprints.uea.ac.uk/id/eprint/85253
DOI: 10.1109/ACCESS.2020.2976624

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