Implementing spam detection using bayesian and porter stemmer keyword stripping approaches

Biju Issac; Wendy J. Jap

doi:10.1109/TENCON.2009.5396056

Implementing spam detection using bayesian and porter stemmer keyword stripping approaches

Biju Issac^*, Wendy J. Jap

^*Corresponding author for this work

Computer and Information Sciences Department

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

35 Citations (Scopus)

Abstract

Unsolicited or spam emails are on the rise, where one's email storage inbox is bombarded with emails that make no sense at all. This creates excess usage of traffic bandwidth and results in unnecessary wastage of network resources. We wanted to test the Bayesian spam detection scheme with context matching that we had developed by implementing the keyword stripping using the Porter Stemmer algorithm. This could make the keyword search more efficient, as the root or stem word is only considered. Experimental results on two public spam corpuses are also discussed at the end.

Original language	English
Title of host publication	TENCON 2009 - 2009 IEEE Region 10 Conference
Publisher	IEEE
ISBN (Electronic)	9781424445479
ISBN (Print)	9781424445479
DOIs	https://doi.org/10.1109/TENCON.2009.5396056
Publication status	Published - 22 Jan 2010
Event	2009 IEEE Region 10 Conference, TENCON 2009 - Singapore, Singapore Duration: 23 Nov 2009 → 26 Nov 2009

Conference

Conference	2009 IEEE Region 10 Conference, TENCON 2009
Country/Territory	Singapore
City	Singapore
Period	23/11/09 → 26/11/09

Keywords

Bayesian approach
Keyword stemming
Spam detection
Spam email

Access to Document

10.1109/TENCON.2009.5396056

Cite this

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title = "Implementing spam detection using bayesian and porter stemmer keyword stripping approaches",

abstract = "Unsolicited or spam emails are on the rise, where one's email storage inbox is bombarded with emails that make no sense at all. This creates excess usage of traffic bandwidth and results in unnecessary wastage of network resources. We wanted to test the Bayesian spam detection scheme with context matching that we had developed by implementing the keyword stripping using the Porter Stemmer algorithm. This could make the keyword search more efficient, as the root or stem word is only considered. Experimental results on two public spam corpuses are also discussed at the end.",

keywords = "Bayesian approach, Keyword stemming, Spam detection, Spam email",

author = "Biju Issac and Jap, {Wendy J.}",

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AU - Jap, Wendy J.

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N2 - Unsolicited or spam emails are on the rise, where one's email storage inbox is bombarded with emails that make no sense at all. This creates excess usage of traffic bandwidth and results in unnecessary wastage of network resources. We wanted to test the Bayesian spam detection scheme with context matching that we had developed by implementing the keyword stripping using the Porter Stemmer algorithm. This could make the keyword search more efficient, as the root or stem word is only considered. Experimental results on two public spam corpuses are also discussed at the end.

AB - Unsolicited or spam emails are on the rise, where one's email storage inbox is bombarded with emails that make no sense at all. This creates excess usage of traffic bandwidth and results in unnecessary wastage of network resources. We wanted to test the Bayesian spam detection scheme with context matching that we had developed by implementing the keyword stripping using the Porter Stemmer algorithm. This could make the keyword search more efficient, as the root or stem word is only considered. Experimental results on two public spam corpuses are also discussed at the end.

KW - Bayesian approach

KW - Keyword stemming

KW - Spam detection

KW - Spam email

U2 - 10.1109/TENCON.2009.5396056

DO - 10.1109/TENCON.2009.5396056

M3 - Conference contribution

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Y2 - 23 November 2009 through 26 November 2009

ER -

Implementing spam detection using bayesian and porter stemmer keyword stripping approaches

Abstract

Conference

Keywords

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