The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Understanding the network-level behavior of spammers
Abstract
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 across time each spamming host is, and characteristics of spamming botnets. We try to answer these questions by analyzing a 17-month trace of over 10 million spam messages collected at an Internet "spam sinkhole", and by correlating this data with the results of IP-based blacklist lookups, passive TCP fingerprinting information, routing information, and botnet "command and control" traces.We find that most spam is being sent from a few regions of IP address space, and that spammers appear to be using transient "bots" that send only a few pieces of email over very short periods of time. Finally, a small, yet non-negligible, amount of spam is received from IP addresses that correspond to short-lived BGP routes, typically for hijacked prefixes. These trends suggest that developing algorithms to identify botnet membership, filtering email messages based on network-leve l properties (which are less variable than email content), and improving the security of the Internet routing infrastructure, may prove to be extremely effective for combating spam.
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Citations
Cites
Understanding BGP misconfiguration
Mahajan, R., Wetherall, D., & Anderson, T. (2002) · Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications · doi:10.1145/633025.633027 · Cited by 533
Cited by
A survey of learning-based techniques of email spam filtering
Blanzieri, E., & Bryl, A. (2008) · Artificial Intelligence Review · doi:10.1007/s10462-009-9109-6 · Cited by 407
Spamming botnets: signatures and characteristics
Xie, Y., Yu, F., Achan, K., Panigrahy, R., Hulten, G., & Osipkov, I. (2008) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1402946.1402979 · Cited by 339
Filtering spam with behavioral blacklisting
Ramachandran, A., Feamster, N., & Vempala, S. (2007) · Proceedings of the 14th ACM conference on Computer and communications security · doi:10.1145/1315245.1315288 · Cited by 264
Self-citationSybillimit: a near-optimal social network defense against sybil attacks
Yu, H., Gibbons, P. B., Kaminsky, M., & Xiao, F. (2010) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2009.2034047 · Cited by 246
Bhushan, B., & Sahoo, G. (2018) · Wireless Personal Communications · doi:10.1007/s11277-017-4962-0 · Cited by 181
Metadata
- Authors
- Ramachandran, A., & Feamster, N.
- Venue
- ACM SIGCOMM Computer Communication Review
- Published
- 2006-08-11
- Volume
- 36
- Issue
- 4
- Pages
- 291-302
- ISSN
- 0146-4833 (ISSN-L)
- OpenAlex
- W2113344319
- Cited by
- 510
- References
- 1
- FWCI
- 147.59
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ramachandran, A., & Feamster, N. (2006). Understanding the network-level behavior of spammers. ACM SIGCOMM Computer Communication Review. https://doi.org/10.1145/1151659.1159947
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.