Bivariate Classification of Malware in JavaScript using Dynamic Analysis

DOI

10.2991/ccis-13.2013.42How to use a DOI?

Keywords

malicious JavaScript, dynamic analysis, classification, caffeine monkey

Abstract

JavaScript is used as an attack vector to infect webpages to gain access to user’s information. We present a tool that will dynamically analyze and perform bivariate classification of webpages as malicious or benign. We categorized the general behavior of JavaScript using datasets of known benign and malicious JavaScript by using a classifier which is trained on the basis of difference between function calls made by malicious and benign JavaScript and identification of Iframe tag in them. A Script is then matched to those ategorizations to classify its behavior as malicious or benign. Here we have developed a light weight malicious JavaScript detection approach which can be used in real time as most of the existing techniques perform offline analysis.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Gupta Yash
AU  - Bansal Divya
AU  - Sofat Sanjeev
PY  - 2013/11
DA  - 2013/11
TI  - Bivariate Classification of Malware in JavaScript using Dynamic Analysis
BT  - Proceedings of the The 1st International Workshop on Cloud Computing and Information Security
PB  - Atlantis Press
SP  - 178
EP  - 182
SN  - 1951-6851
UR  - https://doi.org/10.2991/ccis-13.2013.42
DO  - 10.2991/ccis-13.2013.42
ID  - Yash2013/11
ER  -