Abstract: Message header spam filtering is described. In an embodiment, a message is received that includes header entries arranged in an ordered sequence which indicates a path by which the message was communicated. The header entries are parsed to categorize each header entry as a header type where the header types are listed in the ordered sequence. A quantity of each different header type is determined, and a determination is made as to whether the message is likely a spam message based at least in part on the quantity corresponding to a particular header type. In another embodiment, a numeric representation of the ordered sequence is created where the numeric representation includes unique integers assigned to each different header type. A determination is made as to whether the message is likely a spam message based at least in part on the numeric representation of the ordered sequence of header types.

Abstract: Network domain reputation-based spam filtering is described. In an embodiment, emails are received from a network domain and a reputation of the network domain is established. Additional emails are filtered as they are received to determine a status of each email as spam email or not spam email. An email can be determined to be a spam email based on any one or more of the reputation of the network domain, an authentication status of an email, and other information that can be derived from an email.

Abstract: The present invention involves a system and method that facilitate extracting data from messages for spam filtering. The extracted data can be in the form of features, which can be employed in connection with machine learning systems to build improved filters. Data associated with origination information as well as other information embedded in the body of the message that allows a recipient of the message to contact and/or respond to the sender of the message call be extracted as features. The features, or a subset thereof, can be normalized and/or deobfuscated prior to being employed as features of the machine learning systems. The (deobfuscated) features can be employed to populate a plurality of feature lists that facilitate spam detection and prevention. Exemplary features include an email address, an IP address, a URL, an embedded image pointing to a URL, and/or portions thereof.

Abstract: The subject invention provides for a feedback loop system and method that facilitate classifying items in connection with spam prevention in server and/or client-based architectures. The invention makes uses of a machine-learning approach as applied to spam filters, and in particular, randomly samples incoming email messages so that examples of both legitimate and junk/spam mail are obtained to generate sets of training data. Users which are identified as spam-fighters are asked to vote on whether a selection of their incoming email messages is individually either legitimate mail or junk mail. A database stores the properties for each mail and voting transaction such as user information, message properties and content summary, and polling results for each message to generate training data for machine learning systems. The machine learning systems facilitate creating improved spam filter(s) that are trained to recognize both legitimate mail and spam mail and to distinguish between them.

Abstract: The subject invention provides for a feedback loop system and method that facilitate classifying items in connection with spam prevention in server and/or client-based architectures. The invention makes uses of a machine-learning approach as applied to spam filters, and in particular, randomly samples incoming email messages so that examples of both legitimate and junk/spam mail are obtained to generate sets of training data. Users which are identified as spam-fighters are asked to vote on whether a selection of their incoming email messages is individually either legitimate mail or junk mail. A database stores the properties for each mail and voting transaction such as user information, message properties and content summary, and polling results for each message to generate training data for machine learning systems. The machine learning systems facilitate creating improved spam filter(s) that are trained to recognize both legitimate mail and spam mail and to distinguish between them.