Overview of textual anti-spam filtering techniques
Elecronic mail (E-mail) is an essential communication tool that has been greatly abused by spammers to disseminate unwanted information (messages) and spread malicious contents to Internet users. Current Internet technologies further accelerated the distribution of spam. Effective controls need to b...
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| Format: | Article |
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Academic Journals
2010
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| _version_ | 1796946361555353600 |
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| author | Subramaniam, T. Jalab, H.A. Taqa, A.Y. |
| author_facet | Subramaniam, T. Jalab, H.A. Taqa, A.Y. |
| author_sort | Subramaniam, T. |
| collection | UM |
| description | Elecronic mail (E-mail) is an essential communication tool that has been greatly abused by spammers to disseminate unwanted information (messages) and spread malicious contents to Internet users. Current Internet technologies further accelerated the distribution of spam. Effective controls need to be deployed to countermeasure the ever growing spam problem. Machine learning provides better protective mechanisms that are able to control spam. This paper summarizes most common techniques used for anti-spam filtering by analyzing the e-mail content and also looks into machine learning algorithms such as Naive Bayesian, support vector machine and neural network that have been adopted to detect and control spam. Each machine learning has its own strengths and limitations as such appropriate preprocessing need to be carefully considered to increase the effectiveness of any given machine learning. |
| first_indexed | 2024-03-06T05:30:33Z |
| format | Article |
| id | um.eprints-12177 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:30:33Z |
| publishDate | 2010 |
| publisher | Academic Journals |
| record_format | dspace |
| spelling | um.eprints-121772019-03-20T08:31:47Z http://eprints.um.edu.my/12177/ Overview of textual anti-spam filtering techniques Subramaniam, T. Jalab, H.A. Taqa, A.Y. Q Science (General) Elecronic mail (E-mail) is an essential communication tool that has been greatly abused by spammers to disseminate unwanted information (messages) and spread malicious contents to Internet users. Current Internet technologies further accelerated the distribution of spam. Effective controls need to be deployed to countermeasure the ever growing spam problem. Machine learning provides better protective mechanisms that are able to control spam. This paper summarizes most common techniques used for anti-spam filtering by analyzing the e-mail content and also looks into machine learning algorithms such as Naive Bayesian, support vector machine and neural network that have been adopted to detect and control spam. Each machine learning has its own strengths and limitations as such appropriate preprocessing need to be carefully considered to increase the effectiveness of any given machine learning. Academic Journals 2010 Article PeerReviewed Subramaniam, T. and Jalab, H.A. and Taqa, A.Y. (2010) Overview of textual anti-spam filtering techniques. International Journal of the Physical Sciences, 5 (12). pp. 1869-1882. ISSN 1992-1950, |
| spellingShingle | Q Science (General) Subramaniam, T. Jalab, H.A. Taqa, A.Y. Overview of textual anti-spam filtering techniques |
| title | Overview of textual anti-spam filtering techniques |
| title_full | Overview of textual anti-spam filtering techniques |
| title_fullStr | Overview of textual anti-spam filtering techniques |
| title_full_unstemmed | Overview of textual anti-spam filtering techniques |
| title_short | Overview of textual anti-spam filtering techniques |
| title_sort | overview of textual anti spam filtering techniques |
| topic | Q Science (General) |
| work_keys_str_mv | AT subramaniamt overviewoftextualantispamfilteringtechniques AT jalabha overviewoftextualantispamfilteringtechniques AT taqaay overviewoftextualantispamfilteringtechniques |