On Neural Networks and the future of Spam

Content-based filters (e.g. Keyword filters, heuristics filters, statistical learning filters, pattern recognition neural networks, and so on) use tokens, which are found during message content analysis, to separate spam from legitimate messages. The effectiveness of these token-based filters is due...

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Bibliographic Details
Main Authors: Cosoi, A.C., Vlad, M.S., Sgarciu, V.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Electronic mail
Feature extraction
Filtering
Information filters
Training
Unsolicited electronic mail
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Description
Summary:Content-based filters (e.g. Keyword filters, heuristics filters, statistical learning filters, pattern recognition neural networks, and so on) use tokens, which are found during message content analysis, to separate spam from legitimate messages. The effectiveness of these token-based filters is due to the presence of token signatures (e.g. tokens that are invariant for the many variants of spam messages). In our research, we discovered a new trend of spam messages that have a low frequency of token signatures, thus making them significantly more difficult to identify. Further on, we will describe this new type of spam and also suggest a few modalities to combat the spread of this prototype of the future spam trend.
DOI:10.1109/AQTR.2008.4588917