TY - JOUR
T1 - Detecting Malicious Behaviors in JavaScript Applications
AU - Mao, Jian
AU - Bian, Jingdong
AU - Bai, Guangdong
AU - Wang, Ruilong
AU - Chen, Yue
AU - Xiao, Yinhao
AU - Liang, Zhenkai
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2018/1/17
Y1 - 2018/1/17
N2 - 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.
AB - 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.
KW - JavaScript application
KW - behavior anomaly detection
KW - hybrid mobile app
UR - https://www.scopus.com/pages/publications/85040927550
U2 - 10.1109/ACCESS.2018.2795383
DO - 10.1109/ACCESS.2018.2795383
M3 - 文章
AN - SCOPUS:85040927550
SN - 2169-3536
VL - 6
SP - 12284
EP - 12294
JO - IEEE Access
JF - IEEE Access
ER -