The recent development of the Internet provides attackers with omnipresent media to spread malicious contents. The most prevailing channel is the World Wide Web (WWW) service. Innocent people's computers usually get infected when they browse a malicious web page, and such malicious pages are usually camouflaged with harmless materials on the screen, which makes users hard to beware of the danger. Furthermore, malicious web pages have a tendency to frequently change so that traditional signature matching might not be able to efficiently identify them. In my study, I leveraged Support Vector Machine (SVM), a powerful machine learning technique, to detect malicious web pages. I extracted features from both URLs and page contents and fine-tuned the parameters of SVM to yield the best performance. The experimental results demonstrate that my approach is effective and resistant to the effect of frequent change.