Bioinformatics Techniques for spam detection

Its not a new topic IBM has discovered that it could use many of the pattern detection techniques and analysis used in bioinformatics in other fields as well.

I thought of adding this as bioinformatics is and microarrays are growing in popularity and decided to give few bytes such articles as wel.

Many of these studies are based on the homology detection. Perhaps going forward the techniques used in SNP detection in SNP microarrays might also find use in other fields notably in spam detection and share market analysis or trends analysis

I find some of the presentation onthe web andd from IBm on using the famous Teiresias algorithm, for spam detection

Chung-Kwei applies advanced pattern matching algorithms developed in IBM’s bioinformatics group to spam detection. This new classification algorithm can detect complex patterns in messages that go beyond the simple word or word phrases used in most algorithms.

A technique originally designed to analyse DNA sequences is the latest weapon in the war against spam. An algorithm named Chung-Kwei (after a feng-shui talisman that protects the home against evil spirits) can catch nearly 97 per cent of spam.

Chung-Kwei is based on the Teiresias algorithm, developed by the bioinformatics research group at IBM’s Thomas J Watson Research Center in New York, US. Teiresias was designed to search different DNA and amino acid sequences for recurring patterns, which often indicate genetic structures
that have an important role.

Instead of chains of characters representing DNA sequences, the research group fed the algorithm 65,000 examples of known spam. Each email was treated as a long, DNA-like chain of characters. Teiresias identified six million recurring patterns in this collection, such as “Viagra”.

Each pattern represented a common sequence of letters and numbers that had appeared in more than one unsolicited message. The researchers then ran a collection of known non-spam (dubbed “ham”) through the same process, and removed the patterns that occurred in both groups.

Genuine email Incoming email was given a score based on how many spam patterns it had. A long email that only had a few spammy sentences would get a relatively low score; but one with many patterns spread across the length of the message would score much higher. The Chung-Kwei correctly identified 64,665 of 66,697 test messages as being spam or 96.56 per cent. More importantly, its rate of misidentifying genuine email as spam was just 1 in 6000 messages. Losing a single email in a torrent of spam is a greater failing in a filter than letting the occasional spam email through.

Chung-Kwei deals with common spammer strategies to dodge pattern-recognition schemes, such as replacing the s with a $, as in “increa$e your $ex power” using its built-in tolerance for different, but
functionally equivalent, DNA sequences. Just as in genetic analysis, Teiresias could be taught that CCC and CCU codons both produce the same amino acid, proline, the anti-spam system an be trained to accept $ and s as identical.

IBM intends to include Chung-Kwei in its commercial product, SpamGuru. Justin Mason, who developed SpamAssassin, one of the most popular open-source anti-spam filters, says that Chung-Kwei looks promising.

 

 

 

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