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Detecting Malicious Behaviors in JavaScript Applications

delete2018-01-01
delete17
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OA
AI
毛剑 (Jian Mao) *
J
Jingdong Bian
白广栋 cover
白广栋 (Guangdong Bai)
R
Ruilong Wang
陈越 (Yue Chen)
Y
Yinhao Xiao
Z
Zhenkai Liang
DOI:10.1109/ACCESS.2018.2795383delete
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Abstract

Abstract

En 中文
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.
Keywords:
JavaScript application
hybrid mobile app
behavior anomaly detection
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Journal

IEEE Access cover
IEEE Access
IF:
3.6
Papers:
9.8W
Citations:
29.4W

Organization

S
Singapore Institute of Technology
Scholars:
827
Papers: 762
Citations: 817
B
Beihang University
Scholars:
5.1W
Papers: 4.1W
Citations: 37
G
George Washington University
Scholars:
1.6W
Papers: 1.4W
Citations: 1.7W
N
National University of Singapore
Scholars:
7.5W
Papers: 6.4W
Citations: 11.4W
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