Malicious web page detection based on on-line learning algorithm

• Published in: 2011 International Conference on Machine Learning and Cybernetics Vol. 4; pp. 1914 - 1919
• Main Authors: Wen Zhang, Yu-Xin Ding, Yan Tang, Bin Zhao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2011
• Subjects: Accuracy; Classification algorithms; Feature extraction; Learning systems; Machine learning; Malicious webpage; On-line learning; Prediction algorithms; Semi-supervised learning; Training
• ISBN: 9781457703058; 145770305X
• ISSN: 2160-133X
• DOI: 10.1109/ICMLC.2011.6016954

The Internet has become an indispensable tool in peoples' daily life. It also bring us serious computer security problem. One big security threat comes from malicious webpages. In this paper we study how to detect malicious pages. Since malicious webpages are generated inconstantly, we use on line learning methods to detect malicious webpages. To keep the client side as safe as possible, we do not download the webpages, and analysis webpages' content. We only use URL information to determine if the URL links to a malicious pages. The feature selection methods for URL are discussed, and the performances of different on line learning methods are compared. To improve the performance of on line learning classifiers, an improved on line learning method is proposed, experiments show that this method is effective.