An Informative and Comprehensive Behavioral Characteristics Analysis Methodology of Android Application for Data Security in Brain-Machine Interfacing
Joint Authors
Su, Xin
Gong, Qingbo
Zheng, Yi
Liu, Xuchong
Li, Kuan-Ching
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-10
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Recently, brain-machine interfacing is very popular that link humans and artificial devices through brain signals which lead to corresponding mobile application as supplementary.
The Android platform has developed rapidly because of its good user experience and openness.
Meanwhile, these characteristics of this platform, which cause the amazing pace of Android malware, pose a great threat to this platform and data correction during signal transmission of brain-machine interfacing.
Many previous works employ various behavioral characteristics to analyze Android application (or app) and detect Android malware to protect signal data secure.
However, with the development of Android app, category of Android app tends to be diverse, and the Android malware behavior tends to be complex.
This situation makes existing Android malware detections complicated and inefficient.
In this paper, we propose a broad analysis, gathering as many behavior characteristics of an app as possible and compare these behavior characteristics in several metrics.
First, we extract static and dynamic behavioral characteristic from Android app in an automatic manner.
Second, we explain the decision we made in each kind of behavioral characteristic we choose for Android app analysis and Android malware detection.
Third, we design a detailed experiment, which compare the efficiency of each kind of behavior characteristic in different aspects.
The results of experiment also show Android malware detection performance of these behavior characteristics combine with well-known machine learning algorithms.
American Psychological Association (APA)
Su, Xin& Gong, Qingbo& Zheng, Yi& Liu, Xuchong& Li, Kuan-Ching. 2020. An Informative and Comprehensive Behavioral Characteristics Analysis Methodology of Android Application for Data Security in Brain-Machine Interfacing. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1139408
Modern Language Association (MLA)
Su, Xin…[et al.]. An Informative and Comprehensive Behavioral Characteristics Analysis Methodology of Android Application for Data Security in Brain-Machine Interfacing. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1139408
American Medical Association (AMA)
Su, Xin& Gong, Qingbo& Zheng, Yi& Liu, Xuchong& Li, Kuan-Ching. An Informative and Comprehensive Behavioral Characteristics Analysis Methodology of Android Application for Data Security in Brain-Machine Interfacing. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1139408
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139408