AN EVALUATION OF FILTERING TECHNIQUES IN A NAÏVE BAYESIAN ANTI-SPAM FILTER by

An efficient anti-spam filter that would block all unsolicited messages i.e. spam, without blocking any legitimate messages is a growing need. To address this problem, this report takes a statistically-based approach, employing a Bayesian anti-spam filter, because it is content-based and self-learning (adaptive) in nature. We train the filter, using a large corpus of legitimate messages and spam, and we test the filter using new incoming personal messages. We evaluate four effective filtering techniques available for a Bayesian filter for our purposes. We look at the effectiveness of the technique, and we evaluate its different configurations for different threshold values in order to find an optimal anti-spam filter configuration. Based on cost-sensitive measures, we conclude that additional safety precautions are needed for a Bayesian anti-spam filter to be put into practice.