Cyber deception against DDoS attack using moving target defence framework in SDN IOT-EDGE networks

Conference paper

Galadima, H., Seeam, A. and Ramsurrun, V. 2022. Cyber deception against DDoS attack using moving target defence framework in SDN IOT-EDGE networks. 3rd International Conference on Next Generation Computing Applications (NextComp). Flic-en-Flac, Mauritius 06 - 08 Oct 2022 IEEE. pp. 1-6 https://doi.org/10.1109/NextComp55567.2022.9932172
TypeConference paper
TitleCyber deception against DDoS attack using moving target defence framework in SDN IOT-EDGE networks
AuthorsGaladima, H., Seeam, A. and Ramsurrun, V.
Abstract

Software Defined Networking (SDN) networking paradigm advancements are advantageous, but they have also brought new security concerns. The Internet of Things (IoT) Edge Computing servers provide closer access to cloud services and is also a point of target for availability attacks. The Distributed Denial of Service (DDoS) attacks on SDN IoT-Edge Computing caused by botnet of IoT hosts has compromised major services and is still an impending concern due to the Work From Home virtual office shift attributed by Covid19 pandemic. The effectiveness of a Moving Target Defense (MTD) technique based on SDN for combating DDoS attacks in IoT-Edge networks was investigated in this study with a test scenario based on a smart building. An MTD Reactive and Proactive Network Address
Shuffling Mechanism was developed, tested, and evaluated with results showing successful defence against UDP, TCP SYN, and LAND DDoS attacks; preventing IoT devices from being botnet compromised due to the short-lived network address; and ensuring reliable system performance.

KeywordsIoT, Moving Target Defense, SDN, IDS, DDoS, Deception
Middlesex University ThemeCreativity, Culture & Enterprise
Conference3rd International Conference on Next Generation Computing Applications (NextComp)
Page range1-6
Proceedings Title2022 3rd International Conference on Next Generation Computing Applications (NextComp)
ISBN
Electronic9781665469548
Electronic9781665469531
Paperback9781665469555
PublisherIEEE
Publication dates
Print06 Oct 2022
Online31 Oct 2022
Publication process dates
Deposited10 Oct 2022
Accepted01 Sep 2022
Output statusPublished
Accepted author manuscript
Copyright Statement

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Digital Object Identifier (DOI)https://doi.org/10.1109/NextComp55567.2022.9932172
LanguageEnglish
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