Abstract-Unwanted and malicious messages dominate Email traffic and pose a great threat to the utility of email communications. Reputation systems have been getting momentum as the solution. Such systems extract Email senders behavior data based on global sending distribution, analyze them and assign a value of trust to each IP address sending email messages. We build two models for the classification purpose. One is based on Support Vector Machines(SVM) and the other is Random Forests(RF). Experimental results show that either classifier is effective. RF is slightly more accurate, but more expensive in terms of both time and space. SVM produces similar accuracy in a much faster manner if given modeling parameters. These classifiers can contribute to a reputation system as one source of analysis and increase its accuracy.
Y. Tang, S. Krasser, Y. He, W. Yang, D. Alperovitch REF proposed a system that used the SVM for classification purpose, such system extract email sender behavior data based on global sending distribution, analyze them and assign a value of trust to each IP address sending email message, the Experimental results show that the SVM classifier is effective, accurate and much faster than the Random Forests (RF) Classifier.
Support Vector Machines and Random Forests Modeling for Spam Senders Behavior Analysis
IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference
IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference
Computer Science