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E-mail Classification using Social Network Information
Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.ORCID-id: 0000-0002-8929-7220
Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
2012 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert) PublishedAlternativ tittel
Klassificering av e-post via information från sociala nätverk (svensk)
Abstract [en]

A majority of E-mail is suspected to be spam. Traditional spam detection fails to differentiate between user needs and evolving social relationships. Online Social Networks (OSNs) contain more and more social information, contributed by users. OSN information may be used to improve spam detection. This paper presents a method that can use several social networks for detecting spam and a set of metrics for representing OSN data. The paper investigates the impact of using social network data extracted from an E-mail corpus to improve spam detection. The social data model is compared to traditional spam data models by generating and evaluating classifiers from both model types. The results show that accurate spam detectors can be generated from the low-dimensional social data model alone, however, spam detectors generated from combinations of the traditional and social models were more accurate than the detectors generated from either model in isolation.

sted, utgiver, år, opplag, sider
Prague, Czech Republic: IEEE , 2012.
Emneord [en]
mail, social network, osn, privacy, data mining, machine learning
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-7019DOI: 10.1109/ARES.2012.84Lokal ID: oai:bth.se:forskinfo77D730046FE40181C1257B060049E2A5ISBN: 978-1-4673-2244-7 (tryckt)OAI: oai:DiVA.org:bth-7019DiVA, id: diva2:834591
Konferanse
Seventh International Conference on Availability, Reliability and Security
Tilgjengelig fra: 2013-02-08 Laget: 2013-02-02 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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