Improving antispam techniques by embracing pattern-based filtering
This study attempted to show that there are still away to improve antispam system. The classical method of filtering spam is by inspecting content of an e-mail and finding a matching pattern with a predefined ruleset. Each matched keyword or sentence will produce a weight, also called score, which w...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2009
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/10003/1/HairulAnuarMatMFC2009.pdf http://eprints.utm.my/id/eprint/10003/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:1289 |
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| Summary: | This study attempted to show that there are still away to improve antispam system. The classical method of filtering spam is by inspecting content of an e-mail and finding a matching pattern with a predefined ruleset. Each matched keyword or sentence will produce a weight, also called score, which will be combined to produce the final score and later to be used for identifying spam similarities in the message. Spammers keep changing a style in generating a spam message to avoid being filtered. Bayesian technique was found to be suitable to embed in the antispam in order to recognize the characteristic of spam in a message eventhough the content has been changed. However, the Bayesian introduced difficulties to the system as spammers have changed the way they send the spam specifically to bypass Bayesian filter. Thus, it is time to find a way to filter those spam. Normally an antispam works on the content, but there is a possibility to filter spam based on its pattern of delivering the spam at network level to reduce the congestion in the network. Filter at network level is also benefiting the server as it has eliminated some spam before they are received and processed for the content. A study were conducted to show above statement is true. An Antispam with Pattern Based Filter (ASPBF) will be tested and the result will be compared with the test for Antispam with Bayesian. The comparison result will determine how much it has achieved its objectives. This study will be able to stimulate more studies in the future to further improve antispam solution in the fight against spam and to have a better e-mail communications. |
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