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Detecting Spammers with SNARE: Spatio-temporal Network-level Automatic Reputation Engine
2009
USENIX Security Symposium
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First, we study first-order properties of network-level features that may help distinguish spammers from legitimate senders. ...
We build an automated reputation engine, SNARE, based on these features using labeled data from a deployed commercial spam-filtering system. ...
Towards this goal, this paper presents SNARE (Spatiotemporal Network-level Automatic Reputation Engine), a sender reputation engine that can accurately and automatically classify email senders based on ...
dblp:conf/uss/HaoSFGK09
fatcat:rq3n4hqnkrcqtdvetgux2unogu
Machine Learning for E-mail Spam Filtering: Review,Techniques and Trends
[article]
2016
arXiv
pre-print
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The initial exposition of the background examines the basics of e-mail spam filtering, the evolving nature of spam, spammers playing cat-and-mouse with e-mail service providers (ESPs), and the Machine ...
To improve the state of affairs they presented SNARE -a sender reputation system with robust classification accuracy. ...
[Hao et al, 2009 ] explored various spatio-temporal features of e-mail senders and investigated ways to deduce the reputation of an e-mail sender based only on such features. ...
arXiv:1606.01042v1
fatcat:cblnuc4knfhehjwzjeeekbgf3m
Trust in collaborative web applications
2012
Future generations computer systems
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Such functionality permits individuals to add -and sometimes modify -web content, often with minimal barriers to entry. Ideally, large bodies of knowledge can be amassed and shared in this manner. ...
Such functionality permits individuals to add -and sometimes modify -web content, often with minimal barriers to entry. Ideally, large bodies of knowledge can be amassed and shared in this manner. ...
[65] concentrate on a set of content-exclusive metadata features based on spatio-temporal properties. ...
doi:10.1016/j.future.2011.02.007
fatcat:siwvu6fke5gynafyn6vxketlau
Early detection of malicious web content with applied machine learning EARLY DETECTION OF MALICIOUS WEB CONTENT WITH APPLIED MACHINE LEARNING Title and Department Date EARLY DETECTION OF MALICIOUS WEB CONTENT WITH APPLIED MACHINE LEARNING
2011
unpublished
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We demonstrate that by mining Top-Level DNS data we can produce a candidate set of domains that contains up to 65% of doiv mains that will be blacklisted. ...
The common theme running through our work is the demonstration that we can detect attacks missed by other security tools as well as detecting attacks sooner than other security responses. ...
as a spatio-temporal method for detecting spammers [45] .Another line of work aims to utilize DNS registration and WHOIS information. ...
fatcat:wier7oxmubgzjawmqct2gocwee