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» Detecting Spam at the Network Level
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CCS
2006
ACM
13 years 11 months ago
Can machine learning be secure?
Machine learning systems offer unparalled flexibility in dealing with evolving input in a variety of applications, such as intrusion detection systems and spam e-mail filtering. H...
Marco Barreno, Blaine Nelson, Russell Sears, Antho...
CEAS
2005
Springer
14 years 1 months ago
Experiences with Greylisting
Greylisting temporarily rejects mail from unknown sources on the theory that real mailers will retry while spamware won’t. I outline a taxonomy of greylisters and report some st...
John R. Levine
NETWORK
2008
94views more  NETWORK 2008»
13 years 7 months ago
Detecting compromised routers via packet forwarding behavior
While it is widely understood that criminal miscreants are subverting large numbers of Internet-connected computers (e.g., for bots, spyware, SPAM forwarding), it is less well app...
Alper Tugay Mizrak, Stefan Savage, Keith Marzullo
RAID
2009
Springer
14 years 2 months ago
Regular Expression Matching on Graphics Hardware for Intrusion Detection
The expressive power of regular expressions has been often exploited in network intrusion detection systems, virus scanners, and spam filtering applications. However, the flexibl...
Giorgos Vasiliadis, Michalis Polychronakis, Spyros...
AIRWEB
2007
Springer
14 years 1 months ago
Splog Detection Using Self-similarity Analysis on Blog Temporal Dynamics
This paper focuses on spam blog (splog) detection. Blogs are highly popular, new media social communication mechanisms. The presence of splogs degrades blog search results as well...
Yu-Ru Lin, Hari Sundaram, Yun Chi, Jun'ichi Tatemu...