Artikel in Tagungsbänden
Combating Web Spam with TrustRank..
In:
VLDB, Seiten 576-587.
2004.
Zoltán Gyöngyi, Hector Garcia-Molina und Jan Pedersen.
[doi]
[BibTeX]
Technische Berichte
Personal Email Networks: An Effective Anti-Spam Tool.
University of California, Los Angeles, 2004.
P.O. Boykin und V. Roychowdhury.
[doi]
[BibTeX]
Artikel in Zeitschriften
Good Word Attacks on Statistical Spam Filters.
, 2005.
Daniel Lowd und Christopher Meek.
[doi]
[BibTeX]
Artikel in Tagungsbänden
Thwarting the Nigritude Ultramarine: Learning to Identify Link Spam..
In:
ECML, Seiten 96-107.
2005.
Isabel Drost und Tobias Scheffer.
[doi]
[BibTeX]
Technische Berichte
Link spam detection based on mass estimation.
Stanford Univ., 2005.
Z. Gyongyi, P. Berkhin, H. Garcia-Molina und J. Pedersen.
[doi]
[BibTeX]
Artikel in Tagungsbänden
Content based SMS spam filtering.
In:
DocEng '06: Proceedings of the 2006 ACM symposium on Document engineering, Seiten 107-114.
ACM Press, New York, NY, USA, 2006.
José María Gómez Hidalgo, Guillermo Cajigas Bringas, Enrique Puertas Sánz und Francisco Carrero García.
[doi]
[BibTeX]
Sonstiges
Comparative Graph Theoretical Characterization of Networks of Spam and Legitimate Email.
2005.
Luiz H. Gomes, Rodrigo B. Almeida, Luis M. A. Bettencourt, Virgilio Almeida und Jussara M. Almeida.
[doi]
[Kurzfassung]
[BibTeX]
Email is an increasingly important and ubiquitous means of communication, both facilitating contact between private individuals and enabling rises in the productivity of organizations. However the relentless rise of automatic unauthorized emails, a.k.a. spam is eroding away much of the attractiveness of email communication. Most of the attention dedicated to date to spam detection has focused on the content of the emails or on the addresses or domains associated with spam senders. Although methods based on these - easily changeable - identifiers work reasonably well they miss on the fundamental nature of spam as an opportunistic relationship, very different from the normal mutual relations between senders and recipients of legitimate email. Here we present a comprehensive graph theoretical analysis of email traffic that captures these properties quantitatively. We identify several simple metrics that serve both to distinguish between spam and legitimate email and to provide a statistical basis for models of spam traffic.
Reputation Network Analysis for Email Filtering.
2004.
J. Golbeck und J. Hendler.
[doi]
[BibTeX]
Artikel in Tagungsbänden
Scalable and Reliable Collaborative Spam Filters: Harnessing the Global Social Email Networks..
In:
CEAS.
2005.
Joseph S. Kong, P. Oscar Boykin, Behnam Attaran Rezaei, Nima Sarshar und Vwani P. Roychowdhury.
[doi]
[BibTeX]
The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems.
In:
AIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web, Seiten 61-68.
ACM, New York, NY, USA, 2008.
Beate Krause, Christoph Schmitz, Andreas Hotho und Gerd Stumme.
[doi]
[BibTeX]
Combating spam in tagging systems.
In:
AIRWeb '07: Proceedings of the 3rd international workshop on Adversarial information retrieval on the web, Seiten 57-64.
ACM Press, New York, NY, USA, 2007.
Georgia Koutrika, Frans Adjie Effendi, Zoltán Gyöngyi, Paul Heymann und Hector Garcia-Molina.
[doi]
[BibTeX]
Social spam detection..
In: D. Fetterly und Z. Gyöngyi
(Herausgeber):
AIRWeb, Reihe ACM International Conference Proceeding Series, Seiten 41-48.
2009.
Benjamin Markines, Ciro Cattuto und Filippo Menczer.
[doi]
[BibTeX]
Artikel in Zeitschriften
Fighting Spam on Social Web Sites: A Survey of Approaches and Future Challenges..
IEEE Internet Computing, 11(6):36-45, 2007.
Paul Heymann, Georgia Koutrika und Hector Garcia-Molina.
[doi]
[BibTeX]
Artikel in Tagungsbänden
Towards Understanding Spammers - Discovering Local Patterns for Concept Characterization and Description.
In: J. F. A. Knobbe
(Herausgeber):
Proc. LeGo-09: From Local Patterns to Global Models, Workshop at the 2009 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.
2009.
accepted
Martin Atzmueller, Florian Lemmerich, Beate Krause und Andreas Hotho.
[doi]
[BibTeX]
Who are the Spammers? Understandable Local Patterns for Concept Description.
In:
7th Conference on Computer Methods and Systems.
Krakow, Poland, 2009.
ISBN 83-916420-5-4
Martin Atzmueller, Florian Lemmerich, Beate Krause und Andreas Hotho.
[doi]
[BibTeX]
Artikel in Zeitschriften
Blog track open task: Spam blog classification.
TREC 2006 Blog Track Notebook, 2006.
P. Kolari, A. Java, T. Finin, J. Mayfield, A. Joshi und J. Martineau.
[doi]
[BibTeX]
Artikel in Tagungsbänden
Privacy-aware spam detection in social bookmarking systems.
In:
Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies, Reihe i-KNOW '11, Seiten 15:1-15:8.
ACM, New York, NY, USA, 2011.
Beate Navarro Bullock, Hana Lerch, Alexander Ro Andreas Hotho und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
With the increased popularity of Web 2.0 services in the last years data privacy has become a major concern for users. The more personal data users reveal, the more difficult it becomes to control its disclosure in the web. However, for Web 2.0 service providers, the data provided by users is a valuable source for offering effective, personalised data mining services. One major application is the detection of spam in social bookmarking systems: in order to prevent a decrease of content quality, providers need to distinguish spammers and exclude them from the system. They thereby experience a conflict of interests: on the one hand, they need to identify spammers based on the information they collect about users, on the other hand, they need to respect privacy concerns and process as few personal data as possible. It would therefore be of tremendous help for system developers and users to know which personal data are needed for spam detection and which can be ignored. In this paper we address these questions by presenting a data privacy aware feature engineering approach. It consists of the design of features for spam classification which are evaluated according to both, performance and privacy conditions. Experiments using data from the social bookmarking system BibSonomy show that both conditions must not exclude each other.