Yahoo! Inc.

Stanley Ke Wei

Vishwanath Tumkur Ramarao

Mark Risher

Dragomir Dimitrov Yankov

Anirban Kundu

Lei Zheng

Xiaopeng Xi

Sharat Narayan

Embodiments are directed towards classifying messages as spam using a two phased approach. The first phase employs a statistical classifier to classify messages based on message content. The second phase targets specific message types to capture dynamic characteristics of the messages and identify spam messages using a token frequency based approach. A client component receives messages and sends them to the statistical classifier, which determines a probability that a message belongs to a particular type of class. The statistical classifier further provides other information about a message, including, a token list, and token thresholds. The message class, token list, and thresholds are provided to the second phase where a number of spam tokens in a given message for a given message class are determined. Based on the threshold, the client component then determines whether the message is spam or non-spam.

Spam filtering based on statistics and token frequency modeling

Multiple features of email traffic are analyzed and extracted. Feature vectors comprising the multiple features are created and cluster analysis is utilized to track spam generation even from dynamically changing or aliased IP addresses.

Classification and cluster analysis spam detection and reduction

Growing community of inventors

Palo Alto, CA, United States of America

Stanley Ke Wei