ADAPTIVE SPAM FILTERING BASED ON NAIVE BAYES ALGORITHM

Abstract

Spam filtering is the technique to find out spams. This field is important aspect of text classification. Spam filtering technique is used with email servers, and population of spam is usually more than genuine emails, this is why spam filtering has become important technique. Most of existing spams filtering techniques are unable to detect spam because spammers know how to make spam to reach the destined email account without being filtered. In such situation, naïve bayes spam filter is proved to be a great technique, because several other methods can be integrated with it to improve the performance of spam filter. Hence, it is an important research field in detecting spams. In this dissertation, technique for spam detection and filtering has been proposed based on Naïve Bayes classification technique, which is the existing spam filtering technique. Some enhancements are made in making it adaptive to new kind of spams. In existing spam filtering techniques, static filtering technique has been used, but we proposed dynamic and adaptive filtering technique, which helps in fast and accurate spam detection. Regular training of classifier should be done, database of spam should be updated all the time, and also a particular word should not be always behaved as spam word or a genuine word. Experimental results show that proposed enhancements improves accuracy of spam filtering.