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Spam Filtering at UCLA Math


Current estimates are that 85% to 95% of all E-mail is spam.  

At UCLA math, spam filtering is done on an opt-in basis using the industry-standard SpamAssassin program.  To enable spam filtering, all you need to do is login to your Linux account via SSH or Putty, type "spamscript" and follow the simple menu-driven directions.  Most folks are happy with the default anti-spam settings.

If you choose to do nothing, you will continue to receive all mail addressed to you, both legitimate and spam.

If you do opt-in, your mail will be automatically sorted by the anti-spam filter.  Mail the filter thinks is legitimate goes into your inbox as normal, and and rest will be placed in a mail folder called "spam".  

Please be aware that no anti-spam filter is perfect.  There's always a small chance that legitimate mail will be tagged as spam and automatically filed away in your spam folder (a "false positive").  You should review the contents of your spam folder at regular intervals and look for legitimate E-mail misfiled as spam, and delete everything else there.  There's no reason to save spam.

Every once in a while, spam will sneak past the filter and make it into your inbox (a "false negative").  As long as it doesn't happen too often, your best bet is just to ignore it and delete it.  If, however, you're seeing enough spam slip past the filter to be annoying, there are a couple things you can do.

The easiest thing you can do is increase the sensitivity of the anti-spam filter to spam by using the Advanced Menu option in spamscript to lower your Spam Hit Level.  If you do this, however, you'll likely increase your False Positive rate, so use caution. Our standard recommendation is to decrease your Spam Hit Level by no more than 0.5 points at a time.

If you really, really, hate spam, you can "train" your anti-spam filter using a Bayesian classifier.   To do this, you should save your False Negatives (spam that slipped through) in their own mail folder.  Once you have a critical mass (say, 100 or so messages), you can use the sa-update program to train the filter.  You can also use sa-learn to train the filter to recognize legitimate Email (known as "ham"), making it even more accurate.  For instructions on how to use the sa-learn command go to the Advanced Spam Filtering knowledge base article.

Remember, UCLA IT Staff and the Mathematics Computing Group will NEVER ask you to provide both your username and password via e-mail.