Improved bayesian anti-spam filter - Implementation and analysis on independent spam corpuses

Biju Issac; Wendy Utra Jap; Jofry Hadi Sutanto

doi:10.1109/ICCET.2009.170

Improved bayesian anti-spam filter - Implementation and analysis on independent spam corpuses

Biju Issac^*, Wendy Utra Jap, Jofry Hadi Sutanto

^*Corresponding author for this work

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

15 Citations (Scopus)

Abstract

Spam emails are causing major resource wastage by unnecessarily flooding the network links. Though many antispam solutions have been implemented, the Bayesian spam score approach looks quite promising. A proposal for spam detection algorithm is presented and its implementation using Java is discussed, along with its performance test results on two independent spam corpuses - Ling-spam and Enron-spam. We use the Bayesian calculation for single keyword sets and multiple keywords sets, along with its keyword contexts to improve the spam detection and thus to get good accuracy.

Original language	English
Title of host publication	2009 International Conference on Computer Engineering and Technology
Pages	326-330
Number of pages	5
Volume	2
DOIs	https://doi.org/10.1109/ICCET.2009.170
Publication status	Published - 2 Feb 2009
Externally published	Yes

Keywords

Bayesian approach
Context matching
Keyword sets
Spam corpus
Spam mails

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10.1109/ICCET.2009.170

Cite this

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abstract = "Spam emails are causing major resource wastage by unnecessarily flooding the network links. Though many antispam solutions have been implemented, the Bayesian spam score approach looks quite promising. A proposal for spam detection algorithm is presented and its implementation using Java is discussed, along with its performance test results on two independent spam corpuses - Ling-spam and Enron-spam. We use the Bayesian calculation for single keyword sets and multiple keywords sets, along with its keyword contexts to improve the spam detection and thus to get good accuracy.",

keywords = "Bayesian approach, Context matching, Keyword sets, Spam corpus, Spam mails",

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AB - Spam emails are causing major resource wastage by unnecessarily flooding the network links. Though many antispam solutions have been implemented, the Bayesian spam score approach looks quite promising. A proposal for spam detection algorithm is presented and its implementation using Java is discussed, along with its performance test results on two independent spam corpuses - Ling-spam and Enron-spam. We use the Bayesian calculation for single keyword sets and multiple keywords sets, along with its keyword contexts to improve the spam detection and thus to get good accuracy.

KW - Bayesian approach

KW - Context matching

KW - Keyword sets

KW - Spam corpus

KW - Spam mails

UR - https://www.scopus.com/pages/publications/65949099721

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DO - 10.1109/ICCET.2009.170

M3 - Conference contribution

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EP - 330

BT - 2009 International Conference on Computer Engineering and Technology

ER -

Improved bayesian anti-spam filter - Implementation and analysis on independent spam corpuses

Abstract

Keywords

Access to Document

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