Enhancing big data security with collaborative intrusion detection

Tan, Z; Nagar, UT; He, X; Nanda, P; Liu, RP; Wang, S; Hu, J

Enhancing big data security with collaborative intrusion detection

Tan, Z Nagar, UT He, X

Nanda, P

Liu, RP

Wang, S Hu, J

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Publication Type:: Journal Article
Citation:: IEEE Cloud Computing, 2014, 1 (3), pp. 27 - 33
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Tan, Z	en_US
dc.contributor.author	Nagar, UT	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Nanda, P https://orcid.org/0000-0002-5748-155X	en_US
dc.contributor.author	Liu, RP https://orcid.org/0000-0001-7001-6305	en_US
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Hu, J	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE Cloud Computing, 2014, 1 (3), pp. 27 - 33	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115744
dc.description.abstract	© 2014 IEEE. Big data, often stored in cloud networks, is changing our business models and applications. Rich information residing in big data is driving business decision making to be a data-driven process. The security and privacy of this data, however, have always been a concern of the data owners. Securing cloud computing environments could strengthen data security and privacy. Doing so requires a comprehensive security solution, from attack prevention to attack detection. Intrusion detection systems (IDSs) are playing an increasingly important role in network security schemes. This article studies vulnerabilities in cloud computing and proposes a collaborative IDS framework to enhance the security and privacy of big data.	en_US
dc.relation.ispartof	IEEE Cloud Computing	en_US
dc.relation.isbasedon	10.1109/MCC.2014.53	en_US
dc.title	Enhancing big data security with collaborative intrusion detection	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	1	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
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	1	en_US

Abstract:

© 2014 IEEE. Big data, often stored in cloud networks, is changing our business models and applications. Rich information residing in big data is driving business decision making to be a data-driven process. The security and privacy of this data, however, have always been a concern of the data owners. Securing cloud computing environments could strengthen data security and privacy. Doing so requires a comprehensive security solution, from attack prevention to attack detection. Intrusion detection systems (IDSs) are playing an increasingly important role in network security schemes. This article studies vulnerabilities in cloud computing and proposes a collaborative IDS framework to enhance the security and privacy of big data.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/41203