NDLI: An Approach to Predict Drive-by-Download Attacks by Vulnerability Evaluation and Opcode

Content Provider	IEEE Xplore Digital Library
Author	Adachi, T. Omote, K.
Copyright Year	2015
Description	Author affiliation: Japan Adv. Inst. of Sci. & Technol., Ishikawa, Japan (Adachi, T.; Omote, K.)
Abstract	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.
Starting Page	145
Ending Page	151
File Size	236378
Page Count	7
File Format	PDF
ISBN	9781479919895
DOI	10.1109/AsiaJCIS.2015.17
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-24
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Drive-by-Download Attacks Web pages Predictive models Probability Feature extraction Malware Browsers Supervised Machine Learning
Content Type	Text
Resource Type	Article

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