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Spam or Ham? Characterizing and Detecting Fraudulent "Not Spam" Reports in Web Mail Systems
(Georgia Institute of Technology, 2011)
Web mail providers rely on users to “vote” to quickly and
collaboratively identify spam messages. Unfortunately,
spammers have begun to use large collections of compromised
accounts not only to send spam, but also to ...
Building a Better Mousetrap
(Georgia Institute of Technology, 2007)
Routers in the network core are unable to maintain detailed
statistics for every packet; thus, traffic statistics are often
based on packet sampling, which reduces accuracy. Because
tracking large ("heavy-hitter") traffic ...
Packets with Provenance
(Georgia Institute of Technology, 2008)
Traffic classification and distinction allows network operators
to provision resources, enforce trust, control unwanted
traffic, and traceback unwanted traffic to its source. Today’s
classification mechanisms rely ...
Practical Data-Leak Prevention for Legacy Applications in Enterprise Networks
(Georgia Institute of Technology, 2011)
Organizations must control where private information
spreads; this problem is referred to in the industry as
data leak prevention. Commercial solutions for DLP
are based on scanning content; these impose high overhead
and ...
Fishing for Phishing from the Network Stream
(Georgia Institute of Technology, 2008)
Phishing is an increasingly prevalent social-engineering attack
that attempts identity theft using spoofed Web pages
of legitimate organizations. Unfortunately, current phishing
detection methods are neither complete ...
Understanding the Network-Level Behavior of Spammers
(Georgia Institute of Technology, 2006)
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
persistent (in time) each spamming host is, ...