Classification of DDoS traffic for Industrial Internet of Services using Deep learning approaches

Qaiser, G; Chandrasekaran, S; Chai, R; Zheng, J

Classification of DDoS traffic for Industrial Internet of Services using Deep learning approaches

Qaiser, G Chandrasekaran, S Chai, R

Zheng, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), 2023, 00, pp. 1-9
Issue Date:: 2023-10-30

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Field	Value	Language
dc.contributor.author	Qaiser, G
dc.contributor.author	Chandrasekaran, S
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024
dc.contributor.author	Zheng, J
dc.date	2023-09-16
dc.date.accessioned	2024-05-02T12:02:19Z
dc.date.available	2024-05-02T12:02:19Z
dc.date.issued	2023-10-30
dc.identifier.citation	2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), 2023, 00, pp. 1-9
dc.identifier.isbn	9798350322347
dc.identifier.uri	http://hdl.handle.net/10453/178538
dc.description.abstract	The Industrial Internet of Services IIoS combines traditional industrial systems with digital technologies offering numerous advantages like improved efficiency productivity and resource optimization However the rapid growth of IIoS introduces significant cybersecurity risks Cyber threats including DDoS attacks unauthorized access data breaches and malware infections pose a severe risk to IIoS security Among these threats DDoS attacks have become a significant concern DDoS attacks overwhelm IIoS networks with excessive traffic preventing legitimate users from accessing the network Such attacks can disrupt IIoS systems causing downtime and inaccessible services This study aims to analyze DDoS attacks that target IIoS and explore the effectiveness of deep learning algorithms in detecting DDoS This research analyzes the performance of four deep learning algorithms ultimately finding that the DNN and GRU models achieved remarkably high accuracy rates of 99 This study aims to enhance the ability of Industrial Internet of Services IIoS users to identify potential Distributed Denial of Service DDoS threats leading to improved operational security and optimized production processes By employing the findings of this research users can effectively detect DDoS hazards resulting in enhanced productivity and streamlined processes within the IIoS environment
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)
dc.relation.ispartof	2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things
dc.relation.isbasedon	10.1109/aibthings58340.2023.10292480
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Classification of DDoS traffic for Industrial Internet of Services using Deep learning approaches
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Mount Pleasant, MI, USA
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-02T12:02:05Z
pubs.finish-date	2023-09-17
pubs.publication-status	Published
pubs.start-date	2023-09-16
pubs.volume	00

Abstract:

The Industrial Internet of Services IIoS combines traditional industrial systems with digital technologies offering numerous advantages like improved efficiency productivity and resource optimization However the rapid growth of IIoS introduces significant cybersecurity risks Cyber threats including DDoS attacks unauthorized access data breaches and malware infections pose a severe risk to IIoS security Among these threats DDoS attacks have become a significant concern DDoS attacks overwhelm IIoS networks with excessive traffic preventing legitimate users from accessing the network Such attacks can disrupt IIoS systems causing downtime and inaccessible services This study aims to analyze DDoS attacks that target IIoS and explore the effectiveness of deep learning algorithms in detecting DDoS This research analyzes the performance of four deep learning algorithms ultimately finding that the DNN and GRU models achieved remarkably high accuracy rates of 99 This study aims to enhance the ability of Industrial Internet of Services IIoS users to identify potential Distributed Denial of Service DDoS threats leading to improved operational security and optimized production processes By employing the findings of this research users can effectively detect DDoS hazards resulting in enhanced productivity and streamlined processes within the IIoS environment

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http://hdl.handle.net/10453/178538