Drive-by-download attacks exploit vulnerabilities in Web browsers, and users are unnoticeably downloading malware which accesses to the compromised Web sites. A number of detection approaches and tools against such attacks have been proposed so far. Especially, it is becoming easy to specify vulnerabilities of attacks, because researchers well analyze the trend of various attacks. Unfortunately, in the previous schemes, vulnerability information has not been used in the detection/prediction approaches of drive-by-download attacks. In this paper, we propose a prediction approach of “malware downloading” during drive-by-download attacks (approach-I), which uses vulnerability information.
Our experimental results show our approach-I achieves the prediction rate (accuracy) of 92%, FNR of 15% and FPR of 1.0% using Naive Bayes. Furthermore, we propose an enhanced approach (approach-II) which embeds Opcode analysis (dynamic analysis) into our approach-I (static approach). We implement our approach-I and II, and compare the three approaches (approach-I, II and Opcode approaches) using the same datasets in our experiment. As a result, our approach-II has the prediction rate of 92%, and improves FNR to 11% using Random Forest, compared with our approach-I.