A statistical method analyzes email content to differentiate between legitimate messages and unsolicited bulk email. This mechanism learns from the characteristics of known good and bad email to predict the likelihood of future messages being spam. For example, if an email contains frequent occurrences of words commonly found in spam, the system assigns a higher probability of it being spam.
This type of filtering provides a personalized and adaptive approach to email management, improving inbox organization and security. Its effectiveness lies in its ability to evolve with the changing tactics of spammers. Historically, rule-based filters were common, but their static nature made them easily circumvented. Statistical analysis offers a more robust and dynamic defense.
