openbsd-ports/mail/razor-agents/pkg/DESCR

Vipul's Razor is a distributed, collaborative, spam detection and
filtering network. Through user contribution, Razor establishes a
distributed and constantly updating catalogue of spam in propagation
that is consulted by email clients to filter out known spam. Detection
is done with statistical and randomized signatures that efficiently
spot mutating spam content. User input is validated through reputation
assignments based on consensus on report and revoke assertions which
in turn is used for computing confidence values associated with
individual signatures.
Vipul's Razor is a distributed, collaborative, spam detection and filtering network. Razor establishes a distributed and constantly updating catalogue of spam in propagation. This catalogue is used by clients to filter out known spam. On receiving a spam, a Razor Reporting Agent (run by an end-user or a troll box) calculates and submits a 20-character unique identification of the spam (a SHA Digest) to its closest Razor Catalogue Server. The Catalogue Server echos this signature to other trusted servers after storing it in its database. Prior to manual processing or transport-level reception, Razor Filtering Agents (end-users and MTAs) check their incoming mail against a Catalogue Server and filter out or deny transport in case of a signature match. Catalogued spam, once identified and reported by a Reporting Agent, can be blocked out by the rest of the Filtering Agents on the network. WWW: http://razor.sourceforge.net/ Submitted by Marc Matteo <marcm@lectroid.net> 2001-12-27 16:54:19 -05:00			`Vipul's Razor is a distributed, collaborative, spam detection and`
Update to 2.14. Substantial changes documented at: http://razor.sourceforge.net/docs/whatsnew.html ok lebel@ 2002-09-19 22:18:15 -04:00			`filtering network. Through user contribution, Razor establishes a`
			`distributed and constantly updating catalogue of spam in propagation`
			`that is consulted by email clients to filter out known spam. Detection`
			`is done with statistical and randomized signatures that efficiently`
			`spot mutating spam content. User input is validated through reputation`
			`assignments based on consensus on report and revoke assertions which`
			`in turn is used for computing confidence values associated with`
			`individual signatures.`