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SIGIR
2010
ACM
13 years 11 months ago
Uncovering social spammers: social honeypots + machine learning
Web-based social systems enable new community-based opportunities for participants to engage, share, and interact. This community value and related services like search and advert...
Kyumin Lee, James Caverlee, Steve Webb
ITNG
2007
IEEE
14 years 1 months ago
Efficient Spam Email Filtering using Adaptive Ontology
Email has become one of the fastest and most economical forms of communication. However, the increase of email users has resulted in the dramatic increase of spam emails during th...
Seongwook Youn, Dennis McLeod
DBSEC
2010
319views Database» more  DBSEC 2010»
13 years 9 months ago
Detecting Spam Bots in Online Social Networking Sites: A Machine Learning Approach
As online social networking sites become more and more popular, they have also attracted the attentions of the spammers. In this paper, Twitter, a popular micro-blogging service, i...
Alex Hai Wang
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...
CEAS
2008
Springer
13 years 9 months ago
Do Zebras get more Spam than Aardvarks?
Analysis of traffic logs of email received by a large UK ISP shows considerable disparity between the proportions of spam received by addresses with different first characters. Th...
Richard Clayton