Please use this identifier to cite or link to this item: https://doi.org/10.1109/ACCESS.2018.2795383
Title: Detecting Malicious Behaviors in JavaScript Applications
Authors: Mao, J.
Bian, J.
Bai, G.
Wang, R.
Chen, Y.
Xiao, Y.
Liang, Z. 
Keywords: behavior anomaly detection
hybrid mobile app
JavaScript application
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Mao, J., Bian, J., Bai, G., Wang, R., Chen, Y., Xiao, Y., Liang, Z. (2018). Detecting Malicious Behaviors in JavaScript Applications. IEEE Access 6 : 12284-12294. ScholarBank@NUS Repository. https://doi.org/10.1109/ACCESS.2018.2795383
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Abstract: JavaScript applications are widely used in a range of scenarios, including Web applications, mobile applications, and server-side applications. On one hand, due to its excellent cross-platform support, Javascript has become the core technology of social network platforms. On the other hand, the flexibility of the JavaScript language makes such applications prone to attacks that inject malicious behaviors. In this paper, we propose a detection technique to identify malicious behaviors in JavaScript applications. Our method models an application's normal behavior on function activation, which is used as a basis to detect attacks. We prototyped our solution on the popular JavaScript engine V8 and used it to detect attacks on the android system. Our evaluation shows the effectiveness of our approach in detecting injection attacks to JavaScript applications. © 2013 IEEE.
Source Title: IEEE Access
URI: https://scholarbank.nus.edu.sg/handle/10635/210118
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2018.2795383
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
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