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CEAS
2004
Springer
14 years 1 months ago
Trends in Spam Products and Methods
Geoff Hulten, Anthony Penta, Gopalakrishnan Seshad...
CCS
2010
ACM
13 years 7 months ago
@spam: the underground on 140 characters or less
In this work we present a characterization of spam on Twitter. We find that 8% of 25 million URLs posted to the site point to phishing, malware, and scams listed on popular blackl...
Chris Grier, Kurt Thomas, Vern Paxson, Michael Zha...
AIRWEB
2007
Springer
14 years 1 months ago
Transductive Link Spam Detection
Web spam can significantly deteriorate the quality of search engines. Early web spamming techniques mainly manipulate page content. Since linkage information is widely used in we...
Dengyong Zhou, Chris Burges, Tao Tao
SIGCOMM
2006
ACM
14 years 1 months ago
Understanding the network-level behavior of spammers
This paper studies the network-level behavior of spammers, including: IP address ranges that send the most spam, common spamming modes (e.g., BGP route hijacking, bots), how persi...
Anirudh Ramachandran, Nick Feamster
SIGIR
2008
ACM
13 years 7 months ago
Semi-supervised spam filtering: does it work?
The results of the 2006 ECML/PKDD Discovery Challenge suggest that semi-supervised learning methods work well for spam filtering when the source of available labeled examples diff...
Mona Mojdeh, Gordon V. Cormack