An empirical study of spam traffic and the use of DNS black lists
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Understanding the network-level behavior of spammers
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
HoneySpam: honeypots fighting spam at the source
SRUTI'05 Proceedings of the Steps to Reducing Unwanted Traffic on the Internet on Steps to Reducing Unwanted Traffic on the Internet Workshop
Revealing botnet membership using DNSBL counter-intelligence
SRUTI'06 Proceedings of the 2nd conference on Steps to Reducing Unwanted Traffic on the Internet - Volume 2
Toward Automated Dynamic Malware Analysis Using CWSandbox
IEEE Security and Privacy
Spam Filtering With Dynamically Updated URL Statistics
IEEE Security and Privacy
The ghost in the browser analysis of web-based malware
HotBots'07 Proceedings of the first conference on First Workshop on Hot Topics in Understanding Botnets
Exploiting network structure for proactive spam mitigation
SS'07 Proceedings of 16th USENIX Security Symposium on USENIX Security Symposium
LEET'08 Proceedings of the 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats
Peeking into spammer behavior from a unique vantage point
LEET'08 Proceedings of the 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats
Spamming botnets: signatures and characteristics
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Studying spamming botnets using Botlab
NSDI'09 Proceedings of the 6th USENIX symposium on Networked systems design and implementation
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
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With increasing security measures in network services, remote exploitation is getting harder. As a result, attackers concentrate on more reliable attack vectors like email: victims are infected using either malicious attachments or links leading to malicious websites. Therefore efficient filtering and blocking methods for spam messages are needed. Unfortunately, most spam filtering solutions proposed so far are reactive , they require a large amount of both ham and spam messages to efficiently generate rules to differentiate between both. In this paper, we introduce a more proactive approach that allows us to directly collect spam message by interacting with the spam botnet controllers. We are able to observe current spam runs and obtain a copy of latest spam messages in a fast and efficient way. Based on the collected information we are able to generate templates that represent a concise summary of a spam run. The collected data can then be used to improve current spam filtering techniques and develop new venues to efficiently filter mails.