Malicious Website Detection Based on URLs Static Features

Abstract

Real-time and effectiveness are two basic requirements in URL-based malicious website detection. Based on the analysis of malicious URLs construction pattern, this paper puts forward a method to detect malicious website based on URLs static features. All features used in classification are extracted using statistic and there is no need to online access to obtain additional information about website, which reduces detection time greatly. At the same time, a feature reduction method is given in feature vector construction. Combined with reasonable selection of machine learning algorithm, our method can achieve both high accuracy and high efficiency. A series of comparative experiments proved the effectiveness of our method.