Detection of DDoS attack on smart home infrastructure using artificial intelligence models

Raja, T.V., Ezziane, Z., He, J., Ma, X. ORCID: 0000-0003-0074-4192 and Kazaure, A.W.-Z., 2023. Detection of DDoS attack on smart home infrastructure using artificial intelligence models. In: 2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE. ISBN 9798350331554

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Abstract

The whole web world is concerned and constantly threatened by security intrusion. From the topmost corporate companies to the recently established start-ups, every company focuses on their network, system, and information security as it is the core of any company. Even a simple small security breach can cause a considerable loss to the company and compromises the CIA Triad (Confidentiality, Integrity, and Availability). Security concerns and hacking activities such as Distributed Denial of Service (DDoS) attacks are also experienced within home networks which could be saturated reaching a crashing point. This work focuses on using Artificial Intelligence (AI) and identifying suitable models to train, identify, and detect DDoS attacks. In addition, it aims to implement on smart home datasets and find the best model from those which performs with a high accuracy rate on the smart home dataset. The novelty of this project is identifying one best AI model among many of the existing models that works best on smart home datasets and in identifying and detecting DDoS attacks.

Item Type:	Chapter in book
Description:	Paper presented at 2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Suzhou, China, 14-16 October 2022.
Creators:	Raja, T.V., Ezziane, Z., He, J., Ma, X. and Kazaure, A.W.-Z.
Publisher:	IEEE
Date:	5 April 2023
ISBN:	9798350331554
Identifiers:	Number Type 10.1109/cyberc55534.2022.00014 DOI 1767181 Other
Rights:	© 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.
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	12 Jun 2023 14:06
Last Modified:	19 Sep 2023 07:45
URI:	https://irep.ntu.ac.uk/id/eprint/49181

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