Spam filter analysis

TL;DR

This paper evaluates various spam filtering techniques through simulation, finding genetic algorithms excel at server level and naive Bayesian filters are best for user-level filtering.

Contribution

It provides a comparative analysis of spam filtering methods using simulation, highlighting the effectiveness of genetic algorithms and naive Bayesian filters in different contexts.

Findings

Genetic algorithm-based filters perform best at server level.

Naive Bayesian filters are most suitable for user-level filtering.

Simulation results demonstrate the effectiveness of different techniques.

Abstract

Unsolicited bulk email (aka. spam) is a major problem on the Internet. To counter spam, several techniques, ranging from spam filters to mail protocol extensions like hashcash, have been proposed. In this paper we investigate the effectiveness of several spam filtering techniques and technologies. Our analysis was performed by simulating email traffic under different conditions. We show that genetic algorithm based spam filters perform best at server level and naive Bayesian filters are the most appropriate for filtering at user level.