How is DSPAM different from SpamAssassin?
A. While both share the common goal of eradicating spam, the two solutions bear very different philosophies. Cocktail Approach vs. Centralized Adaptive Learning SpamAssassin is designed with the arsenal (a.k.a cocktail or toolbox) philosophy and aggregates the results from a myriad of different spam detection tests with the hope that at least some of the components should detect an inbound spam. These different tests range from heuristic “rules” which identify specific characteristics in spam to blacklists, and finally to limited Bayesian learning. DSPAM’s philosophy is based on the belief that machine-learning (basic artificial intelligence) can, in and of itself, solve the spam problem without the need for human-maintained rules, inaccurate blacklists, or any hodge-podge of solutions for that matter. DSPAM’s one central spam detection function incorporates advanced, concept-based statistical analysis. This has resulted in levels of accuracy up to ten times that of a human, with very
