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https://doi.org/10.1109/dexa.2...
Article . 2009 . Peer-reviewed
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A Biologically Inspired Method of SPAM Detection

Authors: Paul Gardner-Stephen;

A Biologically Inspired Method of SPAM Detection

Abstract

Many traditional SPAM filters work by analyzing the content of each email message in turn against a set of rules that are used to measure the spaminess of the message. Unfortunately, because spammers have access to these rules, the content of SPAM messages continually changes to evade detection. This is similar to the difficulties the immune system faces in identifying and clearing the Human Immuno-Deficiency Virus (HIV). Intriguingly, some individuals are resistant to HIV. We explore the parallels between HIV and SPAM in order to deduce a method of identifying SPAM that transcends the polymorphic nature of the SPAM message body. This proposed method is based on the group behavior of SPAM messages, rather than on the content of a SPAM message. We are in the process of implementing a SPAM filter that uses the proposed method.

Country
Australia
Related Organizations
Keywords

Biological systems, Unsolicited e-mail, HIV, SPAM filters

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average