Posts: 56
Threads: 25
Joined: Sep 2009
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Abstract
A lot of research has been done in the area of spam filtering and several sophisticated methods using artificial intelligence have been proposed. However, most of the open source spam filters available to- day do not provide consistent accuracy levels. Spam assassin, which is at present said to be best open source spam filter, aims at providing a single solution that can satiate the needs of individuals as well as large organizations. It filters spam at various levels using different methods with the idea that spammers will be blocked at least at one of the levels. However, none of them take concept drift into consideration which is a very dominant factor in achieving consistent accuracy levels. In this project we intend to address this issue by developing a collaborative spam filter that uses a centralized incrementally learning spam rules database to detect spam. The filter is aimed for use at an intranet or organization level. The main idea behind using this technique for an organization is that a collaborative filter along with centralized incremental learning will greatly help in detecting concept drift at the earliest and significantly reduce the chances of a given spam spreading too much in an organization.
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Posts: 62
Threads: 22
Joined: Aug 2009
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0
Abstract
A lot of research has been done in the area of spam _ltering and several sophisticated methods using arti_cial intelligence have been proposed. However, most of the open source spam _lters available to- day do not provide consistent accuracy levels. Spamassassin, which is at present said to be best open source spam _lter, aims at providing a single solution that can satiate the needs of individuals as well as large organizations. It _lters spam at various levels using di_erent methods with the idea that spammers will be blocked atleast at one of the levels. However, none of them take concept drift[1] into consideration which is a very dominant factor in achieving consistent accuracy levels. In this project we intend to address this issue by developing a collaborative spam _lter that uses a centralized incrementally learning spam rules database to detect spam. The _lter is aimed for use at an intranet or organization level. The main idea behind using this technique for an organization is that a collaborative _lter along with centralized incre- mental learning[3] will greatly help in detecting concept drift at the earliest and signi_cantly reduce the chances of a given spam spreading too much in an organization.
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