Welcome to Smargasy Inc.

The Smargasy anti-spam solution "Spamwall" helps you to keep your focus on the really important things.


Introducing Smargasy Spamwall

anti-spam solution

Do you want to "Get Viagra for a great price" or "Make Money Today" or "Get any software almost for free". Have you received messages from people you don’t know saying "I love you" or "Congratulations you have won", "Read this immediately" or even "Forward this to 10 other people or..."

If you think, it is about time to get rid of these messages in your inbox, you should check this out. The Smargasy Spamwall.

Feature Details

Smargasy Spamwall uses a sophisticated score based mechanism to decide if an email should be considered junk. Every in-coming and out-going email is assigned a score. Based on this score, Smargasy Spamwall assigns one of three categories to the email: 

  • Junk
  • Possible Junk
  • Good

Besides Junk and Good, Smargasy Spamwall utilizes a third category called Possible Junk. Occasionally, if an email is too close to being good or junk, it is assigned this category, providing the users to manually decide if they want to mark them as junk or good. By default if an email gets a score less than 60, it is considered good. A score of 100 or higher is considered junk. Score between 60 and 100 is considered Possible Junk.

anti-spam solution
Image source: xeams.com

Scoring criteria

Scoring is done based on several built-in rules. Every rule in the system can take the score either in the positive or negative direction. The final score decides the category of the email. Rules in Smargasy Spamwall can be further divided into two categories:

  • User defined rules
  • Self-learning rules

Several user-defined rules are bundled with Smargasy Spamwall at the time of installation. All of these rules has a default score and are fully user configurable.

Self-learning rules adapt to the environment of your users. For example, it learns from the past history of emails to assign a score to future emails. One such rule is called Bayesian Analysis. Another example of self-learning rule is when a local user sends a message to someone outside the network. Smargasy Spamwall remembers who the recipient is and gives credit to that user if he/she sends a reply back.

Score reasoning

Many spam filtering solutions block messages without giving an adequate reason of why it was selected as junk. Smargasy Spamwall, on the other hand, gives a detail description of why a particular email is considered junk. This description is very useful for administrators who want to fine tune the filtering rules.

Types of rules in Smargasy Spamwall for email filtering

Smargasy Spamwall uses several types of rules to assign a score to an email. These rules are defined below.

IP Based Rules

These rules act on the sender's IP address and include the following. 

RBL - The word "RBL" stands for Real-Time Blackhole List. It refers to several services on the Internet that keeps a database of IP addresses belonging to known spammers, virus sources and other exploits. Smargasy Spamwall queries these servers to check if the IP address exists in such a list.SPF Check - The word "SPF" stands for Sender Policy Framework. It is a mechanism to publish a list of IP address for a given domain. SPF records are used to prevent email forgery on the Internet. Many companies publish their SPF data through their DNS server, which includes a list of IP addresses where an email can originate. Smargasy Spamwall tries to match the SPF record for in-coming messages and assigns a score if a mismatch is found.Black/White listing - Administrators can either black list or white list IP addresses in Smargasy Spamwall. A black listed IP address is assigned a positive score, whereas a white list IP address is assigned a negative score.

Content filters

Finger print analysis - Smargasy Spamwall uses a proprietary method of creating a finger print of every email. This finger print is then compared with future messages to determine if it is part of a bulk-mail campaign.
Image analysis - Embedded images in emails are analyzed for patterns.
Body and Header - Smargasy Spamwall utilizes a two-pass approach to analyze the body of every message. It then compares it with a known list of keywords containing a score. Rules can be specified for the following sections:

  • Subject
  • Body
  • Attachments
  • Header

Custom filter

Custom filters are the most powerful and effective filters used in Smargasy Spamwall to detect junk messages. Often spammers use several tricks to avoid being filtered. These custom filters are specially designed to detect such tricks. We believe that these custom filters are so effective that leaving just these filters on you can eliminate 90% of all junk messages.

Virus detection

There are two types of virus detection in Smargasy Spamwall

  • Finger print analysis
  • Integration with CLAMAV - an open source virus protection

Adaptive filters

Adaptive filters are self-learning filters that gets smarter by analyzing the patterns of previously sent and received emails. These filters include:

  • Bayesian analysis
  • Manually marking messages junk or good
  • Sender history tracking

Challenge response

Challenge response is a mechanism where the system sends a challenge email to the sender to verify if it is a valid message. This type of filter is disabled by default but can be enabled if the users want to use it.