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Auth0 B2B Essentials: SSO, MFA, and RBAC Built In
Unlimited organizations, 3 enterprise SSO connections, role-based access control, and pro MFA included. Dev and prod tenants out of the box.
Auth0's B2B Essentials plan gives you everything you need to ship secure multi-tenant apps. Unlimited orgs, enterprise SSO, RBAC, audit log streaming, and higher auth and API limits included. Add on M2M tokens, enterprise MFA, or additional SSO connections as you scale.
Fast Bayesian spam filter along lines suggested by Paul Graham
The bogofilter package implements a fast Bayesian spam filter as
suggested by Paul Graham in "A Plan For Spam"
<http://www.paulgraham.com/spam.html>. It is written in C. Supported platforms: Linux, FreeBSD, Solaris, OS X, HP-UX, AIX, RISC-OS, ...
...It supports the SMTP, POP3 and IMAP (in a simple form) protocols. Furthermore, among much other features, it is capable of showing HTML mails and can identify spam mails by using a Bayesian filter.
dbacl is a general purpose digramic Bayesian text classifier. It can learn text documents you provide, and then compare new input with the learned categories. It can be used for spam filtering, or within your own shell scripts. Sometimes it plays che
Secure File Transfer for Windows with Cerberus by Redwood
Protect and share files over FTP/S, SFTP, HTTPS and SCP with the #1 rated Windows file transfer server.
Cerberus supports unlimited users and connections on a single IP, with built-in encryption, 2FA, and a browser-based web client — all deployable in under 15 minutes with a 25-day free trial.
bmf is a self contained and extremely efficient Bayesian mail filter. See
Paul Graham's article "A Plan for Spam" for background information. It aims
to be faster, smaller, and more versatile than similar applications.
A Bayesian spam filter for SMTP servers, based on Paul Graham's "A Plan for Spam", http://www.paulgraham.com/spam.html. Includes tools for spam-database maintenance & testing config. parameters. Supports sendmail & postfix; others soon. GPL.
This project applies statistical text categorization techniques to the domain of spam mail filtering. The goal is to produce mail filtering software that can be trained to recognize spam in a particular individual's email. The statistical models produc