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
Preview |
PDF (1-s2.0-S1877050921004671-main)
- Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (753kB) | Preview |
Abstract
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 |
Related URLs: | |
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 |
Downloads
Downloads per month over past year
Actions (login required)
View Item |