A DDoS attack mitigation framework for IoT networks using fog computing

Lawall, Muhammad Aminu, Shaikh, Riaz Ahmed ORCID: https://orcid.org/0000-0001-6666-0253 and Hassan, Syed Raheel (2021) A DDoS attack mitigation framework for IoT networks using fog computing. Procedia Computer Science, 182. pp. 13-20. ISSN 1877-0509

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The advent of 5G which strives to connect more devices with high speed and low latencies has aided the growth IoT network. Despite the benefits of IoT, its applications in several facets of our lives such as smart health, smart homes, smart cities, etc. have raised several security concerns such as Distributed Denial of Service (DDoS) attacks. In this paper, we propose a DDoS mitigation framework for IoT using fog computing to ensure fast and accurate attack detection. The fog provides resources for effective deployment of the mitigation framework, this solves the deficits in resources of the resource-constrained IoT devices. The mitigation framework uses an anomaly-based intrusion detection method and a database. The database stores signatures of previously detected attacks while the anomaly-based detection scheme utilizes k-NN classification algorithm for detecting the DDoS attacks. By using a database containing the attack signatures, attacks can be detected faster when the same type of attack is executed again. The evaluations using a DDoS based dataset show that the k-NN classification algorithm proposed for our framework achieves a satisfactory accuracy in detecting DDoS attacks.

Item Type: Article
Uncontrolled Keywords: anomaly mitigation,classification algorithm,ddos,fog computing,internet of things (iot),computer science(all) ,/dk/atira/pure/subjectarea/asjc/1700
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory
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Depositing User: LivePure Connector
Date Deposited: 30 May 2022 13:31
Last Modified: 02 Sep 2023 01:17
URI: https://ueaeprints.uea.ac.uk/id/eprint/85258
DOI: 10.1016/j.procs.2021.02.003


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