DPIF: A framework for distinguishing unintentional quality problems from potential shilling attacks

Li, M; Sun, Y; Su, S; Tian, Z; Wang, Y; Wang, X

DPIF: A framework for distinguishing unintentional quality problems from potential shilling attacks

Li, M Sun, Y Su, S Tian, Z Wang, Y Wang, X

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Publication Type:: Journal Article
Citation:: Computers, Materials and Continua, 2019, 59 (1), pp. 331 - 344
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Li, M	en_US
dc.contributor.author	Sun, Y	en_US
dc.contributor.author	Su, S	en_US
dc.contributor.author	Tian, Z	en_US
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9582-3445	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	Computers, Materials and Continua, 2019, 59 (1), pp. 331 - 344	en_US
dc.identifier.issn	1546-2218	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133863
dc.description.abstract	Copyright © 2019 Tech Science Press. Maliciously manufactured user profiles are often generated in batch for shilling attacks. These profiles may bring in a lot of quality problems but not worthy to be repaired. Since repairing data always be expensive, we need to scrutinize the data and pick out the data that really deserves to be repaired. In this paper, we focus on how to distinguish the unintentional data quality problems from the batch generated fake users for shilling attacks. A two-steps framework named DPIF is proposed for the distinguishment. Based on the framework, the metrics of homology and suspicious degree are proposed. The homology can be used to represent both the similarities of text and the data quality problems contained by different profiles. The suspicious degree can be used to identify potential attacks. The experiments on real-life data verified that the proposed framework and the corresponding metrics are effective.	en_US
dc.relation.ispartof	Computers, Materials and Continua	en_US
dc.relation.isbasedon	10.32604/cmc.2019.05379	en_US
dc.subject.classification	Applied Mathematics	en_US
dc.title	DPIF: A framework for distinguishing unintentional quality problems from potential shilling attacks	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	59	en_US
utslib.for	0804 Data Format	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
utslib.for	0912 Materials Engineering	en_US
utslib.for	0915 Interdisciplinary Engineering	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 Computer Science
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	59	en_US

Abstract:

Copyright © 2019 Tech Science Press. Maliciously manufactured user profiles are often generated in batch for shilling attacks. These profiles may bring in a lot of quality problems but not worthy to be repaired. Since repairing data always be expensive, we need to scrutinize the data and pick out the data that really deserves to be repaired. In this paper, we focus on how to distinguish the unintentional data quality problems from the batch generated fake users for shilling attacks. A two-steps framework named DPIF is proposed for the distinguishment. Based on the framework, the metrics of homology and suspicious degree are proposed. The homology can be used to represent both the similarities of text and the data quality problems contained by different profiles. The suspicious degree can be used to identify potential attacks. The experiments on real-life data verified that the proposed framework and the corresponding metrics are effective.

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

http://hdl.handle.net/10453/133863