A survey on representation learning efforts in cybersecurity domain

Usman, M; Jan, MA; He, X; Chen, J

A survey on representation learning efforts in cybersecurity domain

Usman, M Jan, MA He, X

Chen, J

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Publication Type:: Journal Article
Citation:: ACM Computing Surveys, 2019, 52 (6)
Issue Date:: 2019-10-01

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Field	Value	Language
dc.contributor.author	Usman, M	en_US
dc.contributor.author	Jan, MA	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Chen, J	en_US
dc.date.issued	2019-10-01	en_US
dc.identifier.citation	ACM Computing Surveys, 2019, 52 (6)	en_US
dc.identifier.issn	0360-0300	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137083
dc.description.abstract	© 2019 Association for Computing Machinery. In this technology-based era, network-based systems are facing new cyber-attacks on daily bases. Traditional cybersecurity approaches are based on old threat-knowledge databases and need to be updated on a daily basis to stand against new generation of cyber-threats and protect underlying network-based systems. Along with updating threat-knowledge databases, there is a need for proper management and processing of data generated by sensitive real-time applications. In recent years, various computing platforms based on representation learning algorithms have emerged as a useful resource to manage and exploit the generated data to extract meaningful information. If these platforms are properly utilized, then strong cybersecurity systems can be developed to protect the underlying network-based systems and support sensitive real-time applications. In this survey, we highlight various cyber-threats, real-life examples, and initiatives taken by various international organizations. We discuss various computing platforms based on representation learning algorithms to process and analyze the generated data. We highlight various popular datasets introduced by well-known global organizations that can be used to train the representation learning algorithms to predict and detect threats. We also provide an in-depth analysis of research efforts based on representation learning algorithms made in recent years to protect the underlying network-based systems against current cyber-threats. Finally, we highlight various limitations and challenges in these efforts and available datasets that need to be considered when using them to build cybersecurity systems.	en_US
dc.relation.ispartof	ACM Computing Surveys	en_US
dc.relation.isbasedon	10.1145/3331174	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Information Systems	en_US
dc.title	A survey on representation learning efforts in cybersecurity domain	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	52	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
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
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	52	en_US

Abstract:

© 2019 Association for Computing Machinery. In this technology-based era, network-based systems are facing new cyber-attacks on daily bases. Traditional cybersecurity approaches are based on old threat-knowledge databases and need to be updated on a daily basis to stand against new generation of cyber-threats and protect underlying network-based systems. Along with updating threat-knowledge databases, there is a need for proper management and processing of data generated by sensitive real-time applications. In recent years, various computing platforms based on representation learning algorithms have emerged as a useful resource to manage and exploit the generated data to extract meaningful information. If these platforms are properly utilized, then strong cybersecurity systems can be developed to protect the underlying network-based systems and support sensitive real-time applications. In this survey, we highlight various cyber-threats, real-life examples, and initiatives taken by various international organizations. We discuss various computing platforms based on representation learning algorithms to process and analyze the generated data. We highlight various popular datasets introduced by well-known global organizations that can be used to train the representation learning algorithms to predict and detect threats. We also provide an in-depth analysis of research efforts based on representation learning algorithms made in recent years to protect the underlying network-based systems against current cyber-threats. Finally, we highlight various limitations and challenges in these efforts and available datasets that need to be considered when using them to build cybersecurity systems.

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http://hdl.handle.net/10453/137083