A Machine Learning Architecture Towards Detecting Denial of Service Attack in IoT

Al-Hadhrami, Y; Hussain, FK

A Machine Learning Architecture Towards Detecting Denial of Service Attack in IoT

Al-Hadhrami, Y Hussain, FK

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2020, 993, pp. 417-429
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Al-Hadhrami, Y
dc.contributor.author	Hussain, FK
dc.date	2020-07-03
dc.date.accessioned	2021-04-09T05:19:32Z
dc.date.available	2021-04-09T05:19:32Z
dc.date.issued	2020-01-01
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2020, 993, pp. 417-429
dc.identifier.isbn	9783030223533
dc.identifier.issn	2194-5357
dc.identifier.issn	2194-5365
dc.identifier.uri	http://hdl.handle.net/10453/147955
dc.description.abstract	Internet of thing is part of our everyday life nowadays. Where millions of devices contented to the internet to collect and share data. Although IoT devices are evolving quickly to the consumer market where smart devices and sensors are becoming one of the main components of many households, IoT sensors and actuators have been also heavily used in the industry where thousands of devices are used to collect and share data for different purposes. With the rapid development of the Internet of Things in different areas, IoT is facing difficulty in securing overall availability of the network due to its heterogeneous nature. There are many types of vulnerability in IoT that can be mitigated with further research, however, in this paper, we have concentrated on distributed denial of Service attack (DDoS) on IoT. In this paper, we propose a machine learning architecture to detect DDoS attacks in IoT networks. The architecture collects IoT network traffic and analyzes the traffic through passing to machine learning model for attack detection. We propose the use of real-time data collection tool to dynamically monitor the network.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Advances in Intelligent Systems and Computing
dc.relation.ispartof	International Conference on Complex, Intelligent, and Software Intensive Systems
dc.relation.ispartofseries	Complex, Intelligent, and Software Intensive Systems
dc.relation.isbasedon	10.1007/978-3-030-22354-0_37
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Machine Learning Architecture Towards Detecting Denial of Service Attack in IoT
dc.type	Conference Proceeding
utslib.citation.volume	993
utslib.location.activity	Sydney, Australia
utslib.for	080303 Computer System Security
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-09T05:19:31Z
pubs.finish-date	2020-07-05
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2020-07-03
pubs.volume	993
dc.location	Switzerland

Abstract:

Internet of thing is part of our everyday life nowadays. Where millions of devices contented to the internet to collect and share data. Although IoT devices are evolving quickly to the consumer market where smart devices and sensors are becoming one of the main components of many households, IoT sensors and actuators have been also heavily used in the industry where thousands of devices are used to collect and share data for different purposes. With the rapid development of the Internet of Things in different areas, IoT is facing difficulty in securing overall availability of the network due to its heterogeneous nature. There are many types of vulnerability in IoT that can be mitigated with further research, however, in this paper, we have concentrated on distributed denial of Service attack (DDoS) on IoT. In this paper, we propose a machine learning architecture to detect DDoS attacks in IoT networks. The architecture collects IoT network traffic and analyzes the traffic through passing to machine learning model for attack detection. We propose the use of real-time data collection tool to dynamically monitor the network.

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http://hdl.handle.net/10453/147955