An Informative and Comprehensive Behavioral Characteristics Analysis Methodology of Android Application for Data Security in Brain-Machine Interfacing
المؤلفون المشاركون
Su, Xin
Gong, Qingbo
Zheng, Yi
Liu, Xuchong
Li, Kuan-Ching
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-03-10
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139408
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر