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A Combined Static and Dynamic Analysis Approach to Detect Malicious Browser Extensions
Joint Authors
Wang, Yao
Cai, Wandong
Lyu, Pin
Shao, Wei
Source
Security and Communication Networks
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-05-02
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Information Technology and Computer Science
Abstract EN
Ill-intentioned browser extensions pose an emergent security risk and have become one of the most common attack vectors on the Internet due to their wide popularity and high privilege.
Once installed, malicious extensions are executed and attempt to compromise a victim’s browser.
To detect malicious browser extensions, security researchers have put forward several techniques.
These techniques primarily concentrate on the usage of API calls by malicious extensions, imposing restricted policies for extensions, and monitoring extension’s activities.
In this paper, we propose a machine-learning-based approach to detect malicious extensions.
We apply static and dynamic techniques to analyse an extension for extracting features.
The analysis process extracts features from the source codes including JavaScript codes, HTML pages, and CSS files and the execution activities of an extension.
To guarantee the robustness of the features, a feature selection method is then applied to retain the most relevant features while discarding low-correlated features.
The detection models based on machine-learning techniques are subsequently constructed by leveraging these features.
As can be seen from evaluation results, our detection model, containing over 4,600 labelled extension samples, is able to detect malicious extensions with an accuracy of 96.52% in validation set and 95.18% in test set, with a false positive rate of 2.38% in validation set and 3.66% in test set.
American Psychological Association (APA)
Wang, Yao& Cai, Wandong& Lyu, Pin& Shao, Wei. 2018. A Combined Static and Dynamic Analysis Approach to Detect Malicious Browser Extensions. Security and Communication Networks،Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1214316
Modern Language Association (MLA)
Wang, Yao…[et al.]. A Combined Static and Dynamic Analysis Approach to Detect Malicious Browser Extensions. Security and Communication Networks No. 2018 (2018), pp.1-16.
https://search.emarefa.net/detail/BIM-1214316
American Medical Association (AMA)
Wang, Yao& Cai, Wandong& Lyu, Pin& Shao, Wei. A Combined Static and Dynamic Analysis Approach to Detect Malicious Browser Extensions. Security and Communication Networks. 2018. Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1214316
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214316