MPEG: a video compression standard for multimedia applications
Communications of the ACM - Special issue on digital multimedia systems
Communications of the ACM
Summary cache: a scalable wide-area web cache sharing protocol
IEEE/ACM Transactions on Networking (TON)
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
New directions in traffic measurement and accounting
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
A statistical approach to the spam problem
Linux Journal
Longest prefix matching using bloom filters
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
IEEE Transactions on Information Theory
An effective defense against email spam laundering
Proceedings of the 13th ACM conference on Computer and communications security
Proceedings of the 16th international conference on World Wide Web
Thwarting E-mail Spam Laundering
ACM Transactions on Information and System Security (TISSEC)
Measurement and classification of humans and bots in internet chat
SS'08 Proceedings of the 17th conference on Security symposium
A survey of learning-based techniques of email spam filtering
Artificial Intelligence Review
Semi Supervised Image Spam Hunter: A Regularized Discriminant EM Approach
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Receiver-oriented design of Bloom filters for data-centric routing
Computer Networks: The International Journal of Computer and Telecommunications Networking
A survey of emerging approaches to spam filtering
ACM Computing Surveys (CSUR)
Humans and bots in internet chat: measurement, analysis, and automated classification
IEEE/ACM Transactions on Networking (TON)
International Journal of Bio-Inspired Computation
SpaDeS: Detecting spammers at the source network
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Statistical-based Bayesian filters have become a popular and important defense against spam. However, despite their effectiveness, their greater processing overhead can prevent them from scaling well for enterprise-level mail servers. For example, the dictionary lookups that are characteristic of this approach are limited by the memory access rate, therefore relatively insensitive to increases in CPU speed. We address this scaling issue by proposing an acceleration technique that speeds up Bayesian filters based on approximate classification. The approximation uses two methods: hash-based lookup and lossy encoding. Lookup approximation is based on the popular Bloom filter data structure with an extension to support value retrieval. Lossy encoding is used to further compress the data structure. While both methods introduce additional errors to a strict Bayesian approach, we show how the errors can be both minimized and biased toward a false negative classification.We demonstrate a 6x speedup over two well-known spam filters (bogofilter and qsf) while achieving an identical false positive rate and similar false negative rate to the original filters.