An Informative and Comprehensive Behavioral Characteristics Analysis Methodology of Android Application for Data Security in Brain-Machine Interfacing

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.

[1]  Byung-Gon Chun,et al.  TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones , 2010, OSDI.

[2]  Tao Xie,et al.  AppContext: Differentiating Malicious and Benign Mobile App Behaviors Using Context , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.

[3]  Zhen Huang,et al.  PScout: analyzing the Android permission specification , 2012, CCS.

[4]  Steve Hanna,et al.  Android permissions demystified , 2011, CCS '11.

[5]  Wei Wang,et al.  Fingerprinting Android malware families , 2018, Frontiers of Computer Science.

[6]  Gianluca Dini,et al.  MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention , 2018, IEEE Transactions on Dependable and Secure Computing.

[7]  Xing Chen,et al.  DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model , 2018, Neurocomputing.

[8]  Hammad Afzal,et al.  AdDroid: Rule-Based Machine Learning Framework for Android Malware Analysis , 2019, Mobile Networks and Applications.

[9]  Witawas Srisa-an,et al.  Significant Permission Identification for Machine-Learning-Based Android Malware Detection , 2018, IEEE Transactions on Industrial Informatics.

[10]  Yanfang Ye,et al.  Deep4MalDroid: A Deep Learning Framework for Android Malware Detection Based on Linux Kernel System Call Graphs , 2016, 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WIW).

[11]  Hieu Le Thanh.  Analysis of Malware Families on Android Mobiles: Detection Characteristics Recognizable by Ordinary Phone Users and How to Fix It , 2013 .

[12]  Isil Dillig,et al.  Apposcopy: semantics-based detection of Android malware through static analysis , 2014, SIGSOFT FSE.

[13]  Alessandra Gorla,et al.  Checking app behavior against app descriptions , 2014, ICSE.

[14]  Ali Feizollah,et al.  Evaluation of Network Traffic Analysis Using Fuzzy C-Means Clustering Algorithm in Mobile Malware Detection , 2018 .

[15]  Abdullah Talha Kabakus,et al.  APK Auditor: Permission-based Android malware detection system , 2015, Digit. Investig..

[16]  Yong Peng,et al.  Discriminative extreme learning machine with supervised sparsity preserving for image classification , 2017, Neurocomputing.

[17]  Shiming He,et al.  De-cloaking Malicious Activities in Smartphones Using HTTP Flow Mining , 2017, KSII Trans. Internet Inf. Syst..

[18]  Simin Nadjm-Tehrani,et al.  Crowdroid: behavior-based malware detection system for Android , 2011, SPSM '11.

[19]  Swarat Chaudhuri,et al.  A Study of Android Application Security , 2011, USENIX Security Symposium.

[20]  Heng Yin,et al.  DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android , 2013, SecureComm.

[21]  Konrad Rieck,et al.  DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket , 2014, NDSS.

[22]  Aziz Mohaisen,et al.  Detecting and Classifying Android Malware Using Static Analysis along with Creator Information , 2015, Int. J. Distributed Sens. Networks.

[23]  Yajin Zhou,et al.  Dissecting Android Malware: Characterization and Evolution , 2012, 2012 IEEE Symposium on Security and Privacy.

[24]  Heng Yin,et al.  DroidScope: Seamlessly Reconstructing the OS and Dalvik Semantic Views for Dynamic Android Malware Analysis , 2012, USENIX Security Symposium.