Malicious web page detection based on feature classification

Malicious code detection is a major concern in computer science community in this decade. With the rapid growth of web applications, web sites have been become the attacker´s main target. Innocent users´ machines become compromised by just visiting a malicious page. This paper presents a malicious web page detection based on static feature classification. We classified features into three groups: explicit features, replicated features, and miscellaneous features. We employed Greasemonkey to develop the detection script. It provides the alert when an innocent user is visiting a malicious page. The accuracy of our detection system is 97.9% with 1.42 % of false positive and 2.76% of false negative. The average detection time is 2.49 seconds per page.