Real time dataset generation framework for intrusion detection systems in IoT

Al-Hadhrami, Y; Hussain, FK

Real time dataset generation framework for intrusion detection systems in IoT

Al-Hadhrami, Y Hussain, FK

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Future Generation Computer Systems, 2020, 108, pp. 414-423
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	Al-Hadhrami, Y
dc.contributor.author	Hussain, FK
dc.date.accessioned	2021-03-23T04:29:22Z
dc.date.available	2021-03-23T04:29:22Z
dc.date.issued	2020-07-01
dc.identifier.citation	Future Generation Computer Systems, 2020, 108, pp. 414-423
dc.identifier.issn	0167-739X
dc.identifier.issn	1872-7115
dc.identifier.uri	http://hdl.handle.net/10453/147480
dc.description.abstract	© 2020 The Internet of Things (IoT) has evolved in the last few years to become one of the hottest topics in the area of computer science research. This drastic increase in IoT applications across different disciplines, such as in health-care and smart industries, comes with a considerable security risk. This is not limited only to attacks on privacy; it can also extend to attacks on network availability and performance. Therefore, an intrusion detection system is essential to act as the first line of defense for the network. IDS systems and algorithms depend heavily on the quality of the dataset provided. Sadly, there has been a lack of work in evaluating and collecting intrusion detection system related datasets that are designed specifically for an IoT ecosystem. Most of the studies published focus on outdated and non-compatible datasets such as the KDD98 dataset. Therefore, in this paper, we aim to investigate the existing datasets and their applications for IoT environments. Then we introduce a real-time data collection framework for building a dataset for intrusion detection system evaluation and testing. The main advantages of the proposed dataset are that it contains features that are explicitly designed for the 6LoWPAN/RPL network, the most widely used protocol in the IoT environment.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Future Generation Computer Systems
dc.relation.isbasedon	10.1016/j.future.2020.02.051
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Distributed Computing
dc.title	Real time dataset generation framework for intrusion detection systems in IoT
dc.type	Journal Article
utslib.citation.volume	108
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
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	2021-03-23T04:29:20Z
pubs.publication-status	Published
pubs.volume	108

Abstract:

© 2020 The Internet of Things (IoT) has evolved in the last few years to become one of the hottest topics in the area of computer science research. This drastic increase in IoT applications across different disciplines, such as in health-care and smart industries, comes with a considerable security risk. This is not limited only to attacks on privacy; it can also extend to attacks on network availability and performance. Therefore, an intrusion detection system is essential to act as the first line of defense for the network. IDS systems and algorithms depend heavily on the quality of the dataset provided. Sadly, there has been a lack of work in evaluating and collecting intrusion detection system related datasets that are designed specifically for an IoT ecosystem. Most of the studies published focus on outdated and non-compatible datasets such as the KDD98 dataset. Therefore, in this paper, we aim to investigate the existing datasets and their applications for IoT environments. Then we introduce a real-time data collection framework for building a dataset for intrusion detection system evaluation and testing. The main advantages of the proposed dataset are that it contains features that are explicitly designed for the 6LoWPAN/RPL network, the most widely used protocol in the IoT environment.

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http://hdl.handle.net/10453/147480