%0 Conference Paper %1 4698194 %A Tang, Yuchun %A Krasser, Sven %A He, Yuanchen %A Yang, Weilai %A Alperovitch, D. %B Global Telecommunications Conference, 2008. IEEE GLOBECOM 2008. IEEE %D 2008 %K related_work thesis %P 1-5 %R 10.1109/GLOCOM.2008.ECP.419 %T Support Vector Machines and Random Forests Modeling for Spam Senders Behavior Analysis %U http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4698194&abstractAccess=no&userType=inst %X 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.

@inproceedings{4698194, 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.}, added-at = {2015-03-24T16:18:16.000+0100}, author = {Tang, Yuchun and Krasser, Sven and He, Yuanchen and Yang, Weilai and Alperovitch, D.}, biburl = {https://www.bibsonomy.org/bibtex/2750937bc2231d406878ace07854b6af3/clemensbaier}, booktitle = {Global Telecommunications Conference, 2008. IEEE GLOBECOM 2008. IEEE}, description = {IEEE Xplore Abstract - Support Vector Machines and Random Forests Modeling for Spam Senders Behavior Analysis}, doi = {10.1109/GLOCOM.2008.ECP.419}, interhash = {e1225d6c0299712a59c8befb0a27d51e}, intrahash = {750937bc2231d406878ace07854b6af3}, issn = {1930-529X}, keywords = {related_work thesis}, month = nov, pages = {1-5}, timestamp = {2015-03-24T16:18:16.000+0100}, title = {Support Vector Machines and Random Forests Modeling for Spam Senders Behavior Analysis}, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4698194&abstractAccess=no&userType=inst}, year = 2008 }