How Spammers Fool Bayesian Filters

How Spammers Fool Bayesian Filters - And How to Stop Them
by: Paul Judge, CTO, CipherTrust, Inc.
Effectively stopping spam over the long-term requires much more than blocking individual IP
addresses and creating rules based on keywords that spammers typically use. The increasing
sophistication of spam tools coupled with the increasing number of spammers in the wild has
created a hyper-evolution in the variety and volume of spam. The old ways of blocking the bad guys
just don’t work anymore.

Examining spam and spam-blocking technology can illuminate how this evolution is taking place and
what can be done to combat spam and reclaim e-mail as the efficient, effective communication tool
it was intended to be.

One method used to combat spam is Bayesian Filtering. Named after Thomas Bayes, an English
mathematician, Bayesian Logic is used in decision making and inferential statistics. Bayesian
Filers maintain a database of known spam and ham, or legitimate email. Once the database is large
enough, the system ranks the words according to the probability they will appear in a spam
message.

Words more likely to appear in spam are given a high score (between 51 and 100), and words likely
to appear in legitimate email are given a low score (between 1 and 50). For example, the words
“free” and “sex” generally have values between 95 and 98, whereas the words “emphasis” or
“disadvantage” may have a score between 1 and 4. Commonly used words such as “the” and “that”, and
words new to the Bayesian filters are given a neutral score between 40 and 50 and would not be
used in the system’s algorithm.

When the system receives an email, it breaks the message down into tokens, or words with values
assigned to them. The system utilizes the tokens with scores on the high and low end of the range
and develops a score for the email as a whole. If the email has more spam tokens than ham tokens,
the email will have a high spam score. The email administrator determines a threshold score the
system uses to allow email to pass through to users.

Bayesian filters are effective at filtering spam and minimizing false positives. Because they
adapt and learn based on user feedback, Bayesian Filers produce better results as they are used
within an organization over time. They are not, however, foolproof. Spammers have learned which
words Bayesian Filters consider spammy and have developed ways to insert non-spammy words into
emails to lower the message’s overall spam score. By adding in paragraphs of text from novels or
news stories, spammers can dilute the effects of high-ranking words. Text insertion has also
caused normally legitimate words that are found in novels or news stories to have an inflated spam
score. This may potentially render Bayesian filters less effective over time.

Another approach spammers use to fool Bayesian filters is to create less spammy emails. For
example, a spammer may send an email containing only the phrase, “Here’s the link…”. This approach
can neutralize the spam score and entice users to click on a link to a Web site containing the
spammer’s message. To block this type of spam, the filter would have to be designed to follow the
link and scan the content of the Web site users are asked to visit. This type of filtering is not
currently employed by Bayesian filters because it would be prohibitively expensive in terms of
server resources and could potentially be used as a method of launching denial of service attacks
against commercial servers.

As with all single-method spam filtering methodologies, Bayesian filters are effective against
certain techniques spammers use to fool spam filters, but are not a magic bullet to solving the
spam problem. Bayesian filters are most effective when combined with other methods of spam
detection.

The Solution
When used individually, each anti-spam technique has been systematically overcome by spammers.
Grandiose plans to rid the world of spam, such as charging a penny for each e-mail received or
forcing servers to solve mathematical problems before delivering e-mail, have been proposed with
few results. These schemes are not realistic and would require a large percentage of the
population to adopt the same anti-spam method in order to be effective. You can learn more about
the fight against spam by visiting our website at www.ciphertrust.com and downloading our
whitepapers.


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About the author:

Dr. Paul Judge is a noted scholar and entrepreneur. He is Chief Technology Officer at CipherTrust,
the industry's largest provider of enterprise email security. The company’s flagship product,
IronMail provides a best of breed enterprise anti spam solution
[http://www.ciphertrust.com/products/spam_and_fraud_protection/index.php] designed to stop spam,
phishing attacks and other email-based threats. Learn more by visiting
www.ciphertrust.com/products/spam_and_fraud_protection
[http://www.ciphertrust.com/products/spam_and_fraud_protection/index.php] today.

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