Spam Filters Explained

Written by Alan Hearnshaw

Spam Filters Explained What do they do? How do they work? Which one is right for me? By Alan Hearnshaw

Spam is a very real problem that many people have to deal with on a daily basis. For those that have decided to do something about it and start to investigaterepparttar options available in spam filtering, this article provides a brief introduction to your options andrepparttar 105888 types of spam filters available.

Despiterepparttar 105889 bewildering array of spam filters available today, all claiming torepparttar 105890 best one “of its kind” there are really just five filtering methodologies in general use today and all products rely on one, or a combination of these:

Content-Based Filters “Inrepparttar 105891 beginning, there were content-based filters.”

These filters scanrepparttar 105892 contents ofrepparttar 105893 and look for tell-tale signs thatrepparttar 105894 message is spam. Inrepparttar 105895 early days of spamming it was quite simple to look out for “Kill Words” such as ”Lose Weight” and mark a message as spam if it was found.

Very soon though, spammers got wise to this and started resorting to all kinds of tricks to get their message pastrepparttar 105896 filters. The days of “obfuscation” had begun. We started getting messages containingrepparttar 105897 phrase “L0se Welght” (Noticerepparttar 105898 zero for “o” and “l” for “i”) and even more bizarre – and sometimes quite ingenious – variations. This rendered basic content-based filters somewhat ineffective, although there are one or two onrepparttar 105899 market now that are clever enough to “see through” theses attempts and still provide good results.

Bayesian Based Filters “The Reverend Bayes comes torepparttar 105900 rescue”

Born in London 1702,repparttar 105901 son of a minister, Thomas Bayes developed a formula which allowed him to determinerepparttar 105902 probability of an event occurring based onrepparttar 105903 probabilities of two or more independent evidentiary events.

Bayesian filters “learn” from studying known good and bad messages. Each message is split into single “word bytes”, or tokens and these tokens are placed into a database along with how often they are found in each kind of message. When a new message arrives to be tested byrepparttar 105904 filter,repparttar 105905 new message is also split into tokens and each token is looked up inrepparttar 105906 database. Extrapolating results fromrepparttar 105907 database and applying a form ofrepparttar 105908 good reverend’s formula, know as a “Naive Bayesian” formula,repparttar 105909 message is given a “spamicity” rating and can be dealt with accordingly.

Bayesian filters typically are capable of achieving very good accuracy rates (>97% is not uncommon), and require very little on-going maintenance.

Whitelist/Blacklist Filters “Who goes there, friend or foe?”

This very basic form of filtering is seldom used on its own nowadays, but can be useful as part of a larger filtering strategy.

A “whitelist” is nothing more than a list of e-mail addresses from which you wish to accept communications. A whitelist filter would only accept messages from these people and all others would be rejected

A “blacklist”, conversely, is a list of e-mail addresses - and sometimes IP Addresses (computer identification addresses) - from which communications will not be accepted.

Anti-Phishing Bill Introduced To Congress

Written by Richard A. Chapo

Sen. Partick J. Leahy has introducedrepparttar Anti-Phishing Act of 2005 to Congress for consideration. The Act would allow federal prosecutors to seek fines of up to $250,000 and prison sentences of up to five years against individuals convicted for promoting phishing scams. Online parody and political speech sites would be excluded from prosecution.

“Phishing” is an online scam used to deceive computer users into giving up personal information such as social security numbers and passwords. Phishing scams usually involve email messages requestingrepparttar 105887 verification of personal information from a familiar business. Readers are provided a link that sends them to what appears to berepparttar 105888 site ofrepparttar 105889 company in question. The reader is then asked to verify their account information by providing their name, address, social security number, account number, etc.

In truth,repparttar 105890 site is an illegal copy ofrepparttar 105891 business in question andrepparttar 105892 reader’s information is collected for later fraudulent use including identity theft. Consumers are estimated to lose hundreds of millions of dollars a year to phishing scams. Undoubtedly, you have received more than a few of these emails.

Phishing emails are most likely to userepparttar 105893 sites of banks, credit card companies, and large retailers. Online companies such as Ebay, PayPal and Earthlink have had similar problems. One particularly aggressive group even scammedrepparttar 105894 site ofrepparttar 105895 IRS.

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