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Topic: Bayesian filtering

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	What is Bayesian filter? - A Word Definition From the Webopedia Computer Dictionary
		Bayesian filtering is predicated on the idea that spam can be filtered out based on the probability that certain words will correctly identify a piece of e-mail as spam while other words will correctly identify a piece of e-mail as legitimate and wanted.
		Bayesian filters examine the words in a body of an e-mail, its header information and metadata, word pairs and phrases and even HTML code that can identify, for example, certain colors that can indicate a spam e-mail.
		Bayesian filters are adaptable in that the filter can train itself to identify new patterns of spam and can be adapted by the human user to adjust to the user’s specific parameters for identifying spam.
	www.webopedia.com /TERM/B/Bayesian_filter.html (385 words)

	Better Bayesian Filtering
		Spam filtering is a subset of text classification, which is a well established field, but the first papers about Bayesian spam filtering per se seem to have been two given at the same conference in 1998, one by Pantel and Lin [2], and another by a group from Microsoft Research [3].
		The reason the filters caught them was that both companies in January switched to commercial email senders instead of sending the mails from their own servers, and both the headers and the bodies became much spammier.
		If you were doing Bayesian filtering in a situation where the ratio of spam to nonspam was consistently very high or (especially) very low, you could probably improve filter performance by incorporating prior probabilities.
	www.paulgraham.com /better.html (4059 words)

	18th Large Installation System Administration Conference — Technical Paper
		Bayesian classification has been able to solve the spam problem for this user population for the present and observable future, with a single wordlist, and with no secondary spam filtering techniques employed.
		Bayesian filtering fell from the limelight nearly as quickly, however, due to predicted difficulties in large-scale or centralized implementations, some success by spammers in defeating early Bayesian implementations using poisoning attacks, assumptions of vulnerabilities common among other content filters, and other issues both real and imagined.
		Bayesian filtering is unfortunately not a turnkey-style solution; while it is possible to implement a Bayesian spam filter (including Bogofilter) by simply following the steps in a HOWTO and running some scripts, best results require that the administrators have some understanding of the theory and how best to apply it to their environment.
	www.usenix.org /events/lisa04/tech/blosser/blosser_html (11069 words)

	The Truth about bayesian Filtering
		Bayesian filtering is based on a mathematical theory which promotes that by assigning spam probability numbers to individual trigger words within a message, the message can be classified as spam or not spam.
		As spammers turn significant energy to building software which cloaks their spam to hide the trigger words and phrases from Bayesian filters, the resulting messages become progressively simpler for pattern based scanners to correctly identify as spam.
		Bayesian filtering as implemented today is little more than a "Duct tape and glue" solution to give an outdated, ineffective paradigm a temporary life extension.
	www.jak.com /bayesian-anti-spam-filters.htm (703 words)

	SpamAssassin book from Packt - Bayesian Filtering Free Chapter
		Although Bayesian statistical analysis is a branch of mathematics, one doesn't necessarily need to understand the mathematics to use SpamAssassin's Bayesian filter.
		Although Bayesian filtering is enabled by default, SpamAssassin will not use the filter until it has learned enough spam and ham emails to be able to make a decision when processing an email.
		Although the Bayesian filter includes the ability to automatically learn from emails it processes, this should not be the only method used to train the Bayesian filter.
	spamassassinbook.packtpub.com /chapter9_preview.htm (2672 words)

	Bayesian filtering -- Facts, Info, and Encyclopedia article (Site not responding. Last check: 2007-10-22)
		Bayesian filtering is the process of using (Click link for more info and facts about Bayesian statistical methods) Bayesian statistical methods to classify documents into categories.
		Bayesian (Click link for more info and facts about email filter) email filters take advantage of (Click link for more info and facts about Bayes' theorem) Bayes' theorem.
		There is recent speculation that even the brain uses Bayesian methods to classify sensory stimuli and decide on behavioural responses.
	www.absoluteastronomy.com /encyclopedia/b/ba/bayesian_filtering.htm (751 words)

	Spam and Bayesian Filtering
		It also explains why the Bayesian approach is the best way to tackle spam once and for all, as it overcomes the obstacles faced by more static technologies such as fllist checking, comparing to databases of known spam and keyword checking.
		Bayesian filtering is based on the principle that most events are dependent and that the probability of an event occurring in the future can be inferred from the previous occurrences of that event.
		Before mail can be filtered using this method, the user needs to generate a database with words and tokens (such as the $ sign, IP addresses and domains, and so on), collected from a sample of spam mail and valid mail (referred to as ‘ham’).
	www.sa-consultinggroup.com /Bayesian_Filtering.html (591 words)

	GFI Software
		In other words, Bayesian filtering is a much more intelligent approach because it examines all aspects of a message, as opposed to keyword checking that classifies a mail as spam on the basis of a single word.
		A Bayesian filter is difficult to fool, as opposed to a keyword filter - An advanced spammer who wants to trick a Bayesian filter can either use fewer words that usually indicate spam (such as free, Viagra, etc), or more words that generally indicate valid mail (such as a valid contact name, etc).
		Bayesian filtering, if implemented the right way and tailored to your company is by far the most effective technology to combat spam.
	www.msexchange.org /resource/gfi-software/white-papers/why-bayesian-filtering-is-the-most-effective-anti-spam-technology.html (1930 words)

	The Advantages of Bayesian Spam Filters
		Bayesian Spam Filters work by analyzing and calculating the probability of the contents in the email being spam, and self-building a list of characteristics of both spam and good elements in the message.
		Bayesian spam filtering technique is a great way of filtering out the spam from reaching your inbox.
		Legitimate emails you receive are different from the spam, and the Bayesian spam filter will assign a lower rate of probability of its being spam.
	www.pcmantra.com /Content/The-Advantages-of-Bayesian-Spam-Filters.htm (513 words)

	The SpamBouncer: Spam Filtering
		Bayesian filters are filters that use the statistical analysis methods of Thomas Bayes, an eighteenth-century British mathematician, to analyze email and determine what is spam.
		From what I've seen, bayesian filtering is effective IF AND ONLY IF each user takes the time to "train" the filter on his or her own email.
		Bayesian filtering is nonetheless a useful tool to fight spam for individual users.
	www.spambouncer.org /aboutspam/filtering.shtml (1023 words)

	The Basics Of Bayesian Spam Filtering
		Bayesian spam filtering has become a popular way to distinguish between legitimate emails and illegitimate spam emails, through a process that uses Bayesian statistical methods.
		Bayesian spam filters are what are known as scoring content-based spam filters.
		The basic difference between the Bayesian spam filters and other simple scoring content based spam filters is that the Bayesian spam filters build the list themselves, as against other filters that depend on a manually built list of characteristics.
	www.pcmantra.com /Content/The-Basics-Of-Bayesian-Spam-Filtering.htm (430 words)

	Introduction to Bayesian Filtering
		Now we’re going to let the filter try to decide if a message is spam or not, based on what we’ve told it about what spam looks like and how it differs from non-spam.
		While other filtering methods may be as effective for now, Bayesian filtering includes an element of changeability that makes it very difficult for spammers to circumvent.
		Bayesian filtering is one method used by Process Software’s PreciseMail Anti-Spam Gateway to keep junk email out of your Inbox.
	www.process.com /precisemail/bayesian_filtering.htm (2982 words)

	Blocking over 98% of spam using Bayesian filtering technology
		The Bayesian approach is the only and best way to tackle spam once and for all, as it overcomes the obstacles faced by more static technologies such as fllist checking, databases of known spam and keyword checking.
		The Bayesian filter, on the other hand, takes note of the company's valid outbound mail (and recognizes "mortgage" as being frequently used in legitimate messages), and therefore has a much better spam detection rate and a far lower false positive rate.
		The Bayesian filter is a new approach to spam that is likely to create a revolution in the sphere of anti-spam software - because it is both intelligent and adaptive.
	www.windowsecurity.com /whitepaper/anti_spam/Blocking_Spam_Bayesian_Filtering.html (1746 words)

	Naive Bayesian filtering \| FREE Outlook Express Spam Filter \| Anti-Spam Blocker Software For Microsoft
		Bayesian filtering gained attention when it was described in the paper A Plan for Spam by Paul Graham, and has become a popular mechanism to distinguish illegitimate spam email from legitimate "ham" email.
		For instance, Bayesian spam filters in will typically have learned a high spam probability for the words "Viagra" and "refinance", but a low spam probability in spam in outlook express (and a high ham probability) for words seen only in ham email, such as the names of friends and family members.
		So, her Bayesian spam filter would learn a higher spam probability for words common to personal-ad-related spam, higher than it would if it were trained on some other user's email.
	www.spamarkov.com /naive_bayesian_filtering (1083 words)

	Bayesian filtering with Express Plus business email client
		Bayesian spam filters mathematically evaluate email message content to determine the probability that it is spam.
		Bayesian filters are adaptable and can learn to identify new patterns of spam by analyzing incoming email.
		The Bayesian filtering is based on content, not email addresses, so this may be the best way to get messages that say good things, but are from the wrong people, into your TRASH and out of your INBOX.
	www.chaossoftware.com /express-plus-bayesian-filtering.asp (777 words)

	iRi 18.11.2002 (Site not responding. Last check: 2007-10-22)
		In this case, the filter examines a message, decides how strongly it believes the message is spam, and then can use the human user's classification of the message (spam or not spam) to powerfully and easily update how it determines whether a message is spam or not.
		If a Bayesian filter catches 90% of current spam, Spam Assassin catches %99 of current spam, and my class project catches %50 of current spam, that does not mean you can string the three of them together and catch %99.95 percent of current spam (even discounting false positives).
		Mechanical filtering systems are currently against the wall; this is their last hurrah, their last stand, their final chance for vindication.
	www.jerf.org /irights/2002/11/18.html (2224 words)

	Spam Hound - Bayesian Filtering
		Bayesian filtering performs a statistical analysis of the words that make up the email to determine if the email is spam or a personal email.
		Spam hound combines Bayesian filtering with the other spam detection techniques using a state of the art Neural Network which learns to accurately detect spam based on your personal email usage.
		Bayesian filtering does however remain effective in detecting personal email since spammers are unlikely to know your personal interests and so email that contain words relating to your interests are easily detected and allowed through.
	www.apocgraphy.com /SpamHound/SpamProtection/Bayesian.htm (139 words)

	Bayesian filtering micro-howto
		The technique of using a Bayesian combination of the spam probabilities ("spamicities") of individual words is described by this article, it is possible to filter spam very effectively, using minimal server resources.
		Bayesian filtering requires such feedback in order to be fully effective.
		When you are registering spam with your Bayesian filter, try to remove randomly generated words from the message using an editor beforehand.
	www.csoft.net /docs/micro/bayes.html.en (422 words)

	Collaborative Filtering Research Papers (Site not responding. Last check: 2007-10-22)
		And collaborative filtering techniques have proven to be an vital component of many such recommender systems as they facilitate the generation of high-quality recommendations by leveraging the preferences of communities of similar users.
		A sec-ond contribution of this paper is a new collaborative filtering algorithm based on factor analysis which appears to be the most accurate method for CF to date.
		Eigentaste is a collaborative filtering algorithm that uses _universal queries_ to elicit real-valued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix.
	jamesthornton.com /cf (8385 words)

	freshmeat.net: Category Reviews - Spam Filters
		Spam filtering is one way to reduce the impact of the problem on the individual user (though it does nothing to reduce the effect of the network traffic generated by spam).
		The filters were further restricted to those that could be executed as standalone programs, read a message from standard input, and indicate via their output or their exit value whether it was spam or not.
		For the Bayesian filters, a training set of 68 spam messages and 68 non-spam messages was used (my email from the second half of June, with a random sample of spam messages from the same period).
	freshmeat.net /articles/view/964 (11892 words)

	Al-Muhajabah's Movable Type Tips: adventures in Bayesian filtering
		Basically, you create a "whitelist" of messages that are definitely not spam, and a fllist of messages that definitely are spam, and a computer program evaluates new messages based on their similarity to messages on your whitelist and fllist in order to determine whether they're spam or not.
		But trying to filter by content is the only rational solution when most spammer email addresses are used once and thrown away.
		Bayesian filtering for comment spam is a clever idea, but standalone MT installations are not the right place to test it.
	www.muhajabah.com /islamicblog/mt-tips/archives/reviews/adventures_in_bayesian_filtering.php (1308 words)

	PrismEmail - Bayesian Filtering FAQ - How Bayesian Works
		Bayesian filtering is a statistical approach to spam filtering.
		When Bayesian statistics is enabled, the system will maintain a statistical file that counts how many times each word has appeared in your real email and in spam.
		Once the Bayesian filter is operating you may still want to leave the other filters enabled since we may be able to help you filter out known spam websites that may not be part of your Bayesian statistics.
	www.prismemail.com /bayesianfaq.php (752 words)

	Anti-spam for email servers
		Bayesian filtering is widely acknowledged by leading experts and publications to be the best way to catch spam (see links below).
		GFI's Bayesian filter uses an advanced mathematical formula and a data set which is 'custom- created' for your installation: The spam data is continuously updated by GFI and is automatically downloaded by GFI MailEssentials, whereas the ham data (valid email) is automatically collected from your outbound mail.
		This means that the Bayesian filter is constantly learning new spam tricks, and signifies that spammers cannot circumvent the dataset used.
	www.gfi.com /mes/meswhy.htm (657 words)

	BBC NEWS \| Technology \| How to make spam unstoppable
		To cut out the junk, many e-mail users have turned to a technology known as Bayesian filtering to spot and stop spam before it reaches their in-box.
		The smart filtering has been so successful that it has already forced a change in the way spam messages are written.
		He was prompted to investigate the weaknesses of Bayesian filters because, although he uses them himself, some messages still get through.
	news.bbc.co.uk /1/hi/technology/3458457.stm (613 words)

	BBC NEWS \| Technology \| How to spot and stop spam
		The technique goes by the formidable name of Bayesian Filtering and uses probability to work out if a mail is junk or real.
		"I think filtering 90% will probably be enough to do it," he said, "that would increase their costs by a factor of 10," says Mr Graham.
		Some are even adopting Bayesian filters to spot the most obvious spam.
	news.bbc.co.uk /2/hi/technology/3014029.stm (667 words)

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