![]() Through the use of various forms of community groups, even the burden of training can be significantly reduced. There are no rules to update, no thresholds to set, and very little systems administration after DSPAM's initial integration. Users simply need to forward spam they receive into the system and DSPAM will automatically learn. While the SpamAssassin project requires over 100+ individuals to maintain, DSPAM manages to delivery significantly higher levels of accuracy with only one primary maintainer and a small pool of patch contributors.ĭSPAM's philosophy includes removing unnecessary human maintenance by means of its learning abilities. We feel that the justification for our philosophy is in the credits. This alone has yielded levels of accuracy peaking at 99.991%. ![]() While DSPAM supports many pre-filters, post-filters, and additional layers of analysis, its central function lies solely in adaptive learning and language analysis. DSPAM breaks down each email into its colloquial components, analyzes the historical data for each component, and determines the most interesting characteristics to judge an email by. This has resulted in levels of accuracy up to ten times that of a human, with very few false positives. ![]() DSPAM's one central spam detection function incorporates advanced, concept-based statistical analysis. 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. These different tests range from heuristic "rules" which identify specific characteristics in spam to blacklists, and finally to limited Bayesian 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. While both share the common goal of eradicating spam, the two solutions bear very different philosophies.Ĭocktail Approach vs. How is DSPAM different from SpamAssassin?Ī. The main differences according to their FAQ: Though this production makes me curious, what can DSPAM do for me that S-A cannot? SpamAssassin has served me well, as it has others. Still, I wonder what this does over Bayseain(sp?). Memory serving correctly Markovian is a process based on probablities and factors. Installation is easier? The Markovian aspect I find interesting. SpamAssassin most certainly has ClamAV integration, and Training Alias from what im aware of. But im looking at the feature set described in this /. Now then, SpamAssassin, that other Spam killer software thingy has always been my choice. A change log and release notes are also available. Much of the documentation has also been rewritten to make installation easier. Other significant enhancements include trusted sender whitelisting, integrated Clam Antivirus and LDAP support, a centralized spam training alias, and a new dependency-free storage driver. Version 3.6 also includes a highly accurate alternative to Bayesian filtering known as Markovian discrimination, based on Bill Yerazunis' research. The most notable change is the series of new features added to make an anti-spam gateway appliance possible (Knoppix anyone?). After six months of development, DSPAM v3.6 has been released.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |