A web page malicious script detection system

Security risks brought by Web page information has been a matter that can no longer be ignored. Malicious script is a major challenge the Web sites security is facing currently. According to the data from the Google Research Centre, more than 10% of Web pages is malicious. Especially in China, the proportion of malicious Web pages has reached 43.21%. This paper presents a detection system which is used to locate the malicious scripts in Web pages. It acquires and builds up malicious code features base, URL of hidden links base etc. based on safety data published on security research Web sites. The Web crawler is applied to collecting Web pages source code in this system and learning algorithm for classification is used to train the classifier. The classification results would be evaluated and improved in the end.