Bayesiane filter for detecting a spam

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Dr. Elma Zanaj
Dr. Elma Zanaj
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Bledi Shkurti
Bledi Shkurti
α Polytechnic University of Tirana Polytechnic University of Tirana

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Bayesiane filter for detecting a spam

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Abstract

The detected of spam messages in terms that better having a spam email in the inbox than a ham message in the junk, has been investigated recently. The main contribution of the paper consists in comparing three antispam filters used more nowadays, and will find that which is filter is of the future. By using filters we will also create some patterns as the result of training with different number of emails. Simulations show that due to the trainging of the filters it will be easier to detect the spams.

References

9 Cites in Article
  1. Jonathan Zdziarski (2005). Ending Spam -Bayesian Content Filtering and the Art of Statistical Language Classification.
  2. SpamCop statistics.
  3. Spam statistics and facts.
  4. Eleanor Cook (2002). Drinking From the Firehose -- SPAM SPAM SPAM: and MORE SPAM!!! What are we to do?.
  5. Aaron Kornblum SMTP Path Analysis -Exposing Zombie Spammers.
  6. Patricia Vit (2012). Melipona favosa Pot-Honey from Venezuela.
  7. Jon Kågström (2005). Impoving naïve Bayesian spam filtering.
  8. (2012). Mozilla Thunderbird - download, install and configure.
  9. Cameron Neylon (2007). Sourceforge for science.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr. Elma Zanaj. 2012. \u201cBayesiane filter for detecting a spam\u201d. Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 12 (GJCST Volume 12 Issue E10): .

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Version of record

v1.2

Issue date

June 2, 2012

Language
en
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The detected of spam messages in terms that better having a spam email in the inbox than a ham message in the junk, has been investigated recently. The main contribution of the paper consists in comparing three antispam filters used more nowadays, and will find that which is filter is of the future. By using filters we will also create some patterns as the result of training with different number of emails. Simulations show that due to the trainging of the filters it will be easier to detect the spams.

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Bayesiane filter for detecting a spam

Dr. Elma Zanaj
Dr. Elma Zanaj Polytechnic University of Tirana
Bledi Shkurti
Bledi Shkurti

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