Ever since Paul Graham published "A Plan for Spam" in August 2002 (prerequisite reading for this article), a lot of people have spent a great deal of time applying statistical methods to automatically classify email messages as spam. Generally, spam identification is a hard problem to solve given that the definition of spam can differ from person to person. Messages erroneously classified as spam, known as "false positives," are pretty much intolerable, which further compounds the problem. Statisitical classifiers show great promise in this area as they are able to automatically adjust to handle personal definitions of spam. The odd false positive shows up from time to time, but these become few and far between as the local statistical model continues to improve. . These classifiers already come in many forms. There are POP3 proxies, IMAP proxies, mail file processors, and even classifiers built directly into mail clients. I use POPFile (a na?ve Bayesian classifier in a POP3 proxy) at home with great success. Some work better than others, but with a little training, they all seem to work pretty well. Unfortunately, they have a common shortcoming: They don't cause the spammers any pain. And we all want to cause spammers pain. None of these classifiers are capable of causing the spammers any pain because the spammer is long gone by the time the classifier has the opportunity to process the message. What we need is a way to use the classifier against the spammer while the spammer is still connected. . Combat spam and reduce false positives with Naive Bayes classifiers, ensemble methods like Random Forests, and effective feature engineering strategies. Spam Detection, Classification System, Bayesian Classifier, Email Filtering. . LinuxSecurity.com Team
Spam email is the plague of the 21st century; SpamBayes is its cure. This client-side application analyzes all incoming email messages and automatically sorts out those that are unwanted. SpamBayes digests the contents of email messages and counts how often certain words -- e.g. Viagra -- occur in spam (bad) or ham (good) messages. Based on these word patterns, it calculates an overall score that rates a message as spam, ham, or unknown. You can manually classify unknown mail as spam or ham and SpamBayes will learn accordingly. . The SpamBayes classification sorts out virtually all spam messages and almost never produces a false positive -- that is, a good message wrongly identified as spam. Only once have I had to fetch an email from the junk mail folder. This happened when a Spanish friend wrote me, presumably because Spanish messages are rare in my inbox. I corrected the wrong classification, and all her subsequent messages were recognized as good. The program improves precision with each manual correction. SpamBayes can be run as an Outlook plugin under Windows or as a POP3 or IMAP proxy under Windows, Linux/Unix, and Mac OS. The link for this article located at Newsforge is no longer available. . The SpamBayes classification sorts out virtually all spam messages and almost never produces a false. email, plague, century, spambayes, client-side, application. . LinuxSecurity.com Team
Security continues to be the biggest concern for IT managers and, in turn, design engineers developing firewall systems. With more viruses popping up and hackers attacking more often, corporations are looking for any approach possible to plug holes in their firewall . . . . Security continues to be the biggest concern for IT managers and, in turn, design engineers developing firewall systems. With more viruses popping up and hackers attacking more often, corporations are looking for any approach possible to plug holes in their firewall architectures. Traditionally, designers have turned to packet classification, also called stateless classification, as a means for providing higher levels of performance in a firewall architecture. While doing a nice job on analyzing an individual packet, the packet classification approach falls short. Specifically, by not relating individual packet information to an overall flow, these classification engines can leave big holes in the firewall architecture, requiring application-level proxying, which adds cost and degrades firewall performance. What's needed is a more stateful approach to classification. Rather than simply looking at a packet, designers need to implement stateful classification techniques that allow designers to classify the properties of a packet as well as understand how that packet fits into an overall communication flow. The link for this article located at CommsDesign is no longer available. . In today’s cybersecurity realm, a stateful firewall architecture emerges as a key strategy for bolstering network security through detailed traffic analysis and management.. Firewall Design, Stateful Classification, Packet Analysis. . Anthony Pell
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