Email Security Products





Screen Shots



Main Screen

The first screen in the GEE Whiz Web Console that you will see contains the statistics for GEE Whiz.
Main Screen

General Screen

The general options screen allows control over logging levels for better tracking of any problems with server behavior. Logs can be purged if they are older than a specified date.
General Screen

Anti Virus Configuration

The anti-virus configuration screens allow the user to alter how their virus scanner works in conjuction with GEE Whiz.
Anti Virus Configuration

Spam Control Configuration

The SPAM control configuration screens allow users to specify several parameters in order to better eliminate SPAM in their environment. This section includes the ability to enable baysian classifier SPAM detection which greatly enhances GEE Whizs ability to detect and stop SPAM.
Spam Control Configuration

Filtering Configuration

The filtering configuration options for GEE Whiz allow you to set parameters for email header and content filtering. There are several options for configuring how users are notified that GEE Whiz filtered an email.
Filtering Configuration

Signature Modification Screen

A signature can be automatically added to all mail processed by GEE Whiz. Useful applications of the signature may be confidentiality notices that a lot of companys need attached to all communications coming from their organisation.
Signature Modification Screen

Rulesets Configuration Screen

Rulesets in GEE Whiz are powered by Spam Assassin and can be very powerful tools in detection of new and current SPAM threats.
Rulesets Configuration Screen

RBLs Configuration Screen

Real Time Black-Hole Lists (RBLs) are lists of blacklisted internet domains that are drawn dynamically from online RBL databases. Scores can then be assigned to these RBLs and added to your GEE Whiz SPAM score.
RBLs Configuration Screen

Baysian Classifier Settings

Baysian Classifier is a powerful SPAM detection tool that uses thousands of tokens that are generated from HAM (good email) and SPAM (bad email) and then used to detect language patterns and typical spammer behaviours. The baysian classifier can be taught to be more accurate for your environment if you collect a large sample of HAM and SPAM and store it as an email corpus.
Baysian Classifier Settings

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