| Botnet spam campaigns can be long lasting: evidence, implications, and analysis |
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Joint International Conference on Measurement and Modeling of Computer Systems
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Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
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Seattle, WA, USA
SESSION: Security
table of contents
Pages 13-24
Year of Publication: 2009
ISBN:978-1-60558-511-6
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Authors
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Abhinav Pathak
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Purdue University, West Lafayette, USA
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Feng Qian
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University of Michigan, Ann Arbor, USA
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Y. Charlie Hu
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Purdue University, West Lafayette, USA
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Z. Morley Mao
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University of Michigan, Ann Arbor, USA
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Supranamaya Ranjan
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Narus, Inc., Mountain View, USA
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Downloads (6 Weeks): 75, Downloads (12 Months): 157, Citation Count: 0
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ABSTRACT
Accurately identifying spam campaigns launched by a large number of bots in a botnet allows for accurate spam campaign signature generation and hence is critical to defeating spamming botnets. The straight-forward approach of clustering all spam containing the same label such as an URL into a campaign can be easily defeated by techniques such as simple obfuscations of URLs. In this paper, we perform a comprehensive study of content-agnostic characteristics of spam campaigns, e.g. duration and source-network distribution of spammers, in order to ascertain whether and how they can assist the simple label-based clustering methods in identifying campaigns and generating campaign signatures. In particular, from a five-month trace collected by a relay sinkhole, we manually identified and then analyzed seven URL-based botnet spam campaigns consisting of 52 million spam messages sent over 2.09 million SMTP connections originated from over 150,000 non-proxy spamming hosts and destined to about 200,000 end domains. Our analysis shows that the spam campaigns, when observed from large destination domains, exhibit durations far longer than the five-day period as reported in a recent study. We analyze the implications of this finding on spam campaign signature generation. We further study other characteristics of these long-lasting campaigns. Our analysis reveals several new findings regarding workload distribution, sending patterns, and coordination among the spamming machines.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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