Indian Journal of Science and Technology

Abstract

Objective: To study the detection of malicious web page in mobile environment by lowering the false positive rate (FPR) and False Negative Rate (FNR) in real time and also how this CMW blocks the access of malicious webpages to the user are to be studied. Methods/Statistical Analysis: Now a days, Mobile devices are progressively being used to access the webpages. Content, Layout size, Functionaity have commonly been used to perform the static analysis to check the maliciousness in desktop space. In this paper we have presented a methodology named as CMW (Classification of malicious webpages) which detects the malicious mobile webpages in mobile environment. Here we used static features of mobile webpages derived from the HTML and Javascript content, URL and leading mobile specific capabilities. We then collected over 3500 mobile benign and malicious webpages. Findings: We have extracted a feature set consists of 44 features. 11 of which are not previously identified nor used. we then used a binomial classification technique to build a model for CMW to provide 90% accuracy and 89% true positive rate. It also detects a number of malicious webpages which are not accurately detected by existing techniques such as VirusTotal and Google Safe Browsing. Application/Improvements: We have used 11 new features in our feature set. Due to these new features, the detection of malicious webpages rate will be increased and false positive and false negative rates are reduced.

Keywords: Classification, Features, Machine Learning, Malicious Web Pages, Static Detection