A unified host-based intrusion detection framework using spark in cloud

Liu, M; Xue, Z; He, X

A unified host-based intrusion detection framework using spark in cloud

Liu, M Xue, Z He, X

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020, 2021, 00, pp. 97-103
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Liu, M
dc.contributor.author	Xue, Z
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X
dc.date	2020-12-29
dc.date.accessioned	2022-01-04T03:31:02Z
dc.date.available	2022-01-04T03:31:02Z
dc.date.issued	2021
dc.identifier.citation	Proceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020, 2021, 00, pp. 97-103
dc.identifier.isbn	9780738143804
dc.identifier.issn	2324-898X
dc.identifier.issn	2324-9013
dc.identifier.uri	http://hdl.handle.net/10453/152650
dc.description.abstract	The host-based intrusion detection system (HIDS) is an essential research domain of cybersecurity. HIDS examines log data of hosts to identify intrusive behaviors. The detection efficiency is a significant factor of HIDS. Traditionally, HIDS is often installed with a standalone mode. Training detection engines with a large amount of data on a single physical computer with limited computing resources may be time-consuming. Therefore, this paper offers a unified HIDS framework based on Spark and deployed in the Google cloud. The framework includes a unified machine learning pipeline to implement scalable and efficient HIDS.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020
dc.relation.ispartof	IEEE International Conference on Trust, Security and Privacy in Computing and Communications
dc.relation.isbasedon	10.1109/TrustCom50675.2020.00026
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A unified host-based intrusion detection framework using spark in cloud
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Guangzhou, China
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 - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-01-04T03:31:01Z
pubs.finish-date	2021-01-01
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2020-12-29
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

The host-based intrusion detection system (HIDS) is an essential research domain of cybersecurity. HIDS examines log data of hosts to identify intrusive behaviors. The detection efficiency is a significant factor of HIDS. Traditionally, HIDS is often installed with a standalone mode. Training detection engines with a large amount of data on a single physical computer with limited computing resources may be time-consuming. Therefore, this paper offers a unified HIDS framework based on Spark and deployed in the Google cloud. The framework includes a unified machine learning pipeline to implement scalable and efficient HIDS.

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http://hdl.handle.net/10453/152650