Stealing reality: When criminals become data scientists (or vice versa)

Yaniv Altshuler; Nadav Aharony; Alex Pentland; Yuval Elovici; Manuel Cebrian

doi:10.1109/MIS.2011.78

Stealing reality: When criminals become data scientists (or vice versa)

Yaniv Altshuler, Nadav Aharony, Alex Pentland, Yuval Elovici, Manuel Cebrian

Department of Software and Information Systems Engineering

Research output: Contribution to journal › Article › peer-review

29 Scopus citations

Abstract

Stealing-reality attacks attempt to steal social network and behavioral information through data collection and inference techniques, making them more dangerous than other types of identity theft.

Original language	English
Article number	6030866
Pages (from-to)	22-30
Number of pages	9
Journal	IEEE Intelligent Systems
Volume	26
Issue number	6
DOIs	https://doi.org/10.1109/MIS.2011.78
State	Published - 1 Nov 2011

Keywords

advanced persistent threat
intelligent systems
malware
mobile networks security
social and economic computing
stealth attacks

ASJC Scopus subject areas

Computer Networks and Communications
Artificial Intelligence

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10.1109/MIS.2011.78

Cite this

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title = "Stealing reality: When criminals become data scientists (or vice versa)",

abstract = "Stealing-reality attacks attempt to steal social network and behavioral information through data collection and inference techniques, making them more dangerous than other types of identity theft.",

keywords = "advanced persistent threat, intelligent systems, malware, mobile networks security, social and economic computing, stealth attacks",

author = "Yaniv Altshuler and Nadav Aharony and Alex Pentland and Yuval Elovici and Manuel Cebrian",

note = "Funding Information: This material is based upon work supported by the US National Science Foundation under grant 0905645. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF.",

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AU - Pentland, Alex

AU - Elovici, Yuval

AU - Cebrian, Manuel

N1 - Funding Information: This material is based upon work supported by the US National Science Foundation under grant 0905645. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF.

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Y1 - 2011/11/1

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AB - Stealing-reality attacks attempt to steal social network and behavioral information through data collection and inference techniques, making them more dangerous than other types of identity theft.

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KW - intelligent systems

KW - malware

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KW - social and economic computing

KW - stealth attacks

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Stealing reality: When criminals become data scientists (or vice versa)

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

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