Malicious Behavior Analysis of Android GUI Based on ADB

被引:3
|
作者
Yang, Li [1 ]
Wang, Lijun [1 ]
Zhang, Dongdong [1 ]
机构
[1] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China
基金
中国国家自然科学基金;
关键词
Android; ADB; Activity hijacking;
D O I
10.1109/CSE-EUC.2017.211
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The Android application is part of people's lives, however the functionality required by various user has greatly exceeded its original design. As a result, one must seek other ways to gain permission that is not directly available to the user. A typical approach is using the Android Debug Bridge (ADB), a developer tool that is used to grant permission to critical system resources. There are millions of downloads on Google Play that using this method. However, we found that ADB level functionality is not well protected by Android. A striking example of our investigation is that the ADB tool can be used to get the system application logs. Based on this finding, malicious applications can intelligently gather logs of application activity and then perform hijacking attacks. To understand this threat, we have developed an application that can detect the login time of the target application and then carry out the Activity hijacking attack, so as to obtain his account and password.
引用
收藏
页码:147 / 153
页数:7
相关论文
共 50 条
  • [1] Analysis of Malicious Behavior of Android Apps
    Singh, Pooja
    Tiwari, Pankaj
    Singh, Santosh
    [J]. PROCEEDINGS OF INTERNATIONAL CONFERENCE ON COMMUNICATION, COMPUTING AND VIRTUALIZATION (ICCCV) 2016, 2016, 79 : 215 - 220
  • [2] LSTM Android Malicious Behavior Analysis Based on Feature Weighting
    Yang, Qing
    Wang, Xiaoliang
    Zheng, Jing
    Ge, Wenqi
    Bai, Ming
    Jiang, Frank
    [J]. KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2021, 15 (06): : 2188 - 2203
  • [3] Android App Malicious Behavior Detection Based on User Intention
    Fu, JianMing
    Li, PengWei
    Lin, Yan
    Ding, Shuang
    [J]. 2016 IEEE TRUSTCOM/BIGDATASE/ISPA, 2016, : 560 - 567
  • [4] Detecting Applications with Malicious Behavior in Android Device Based on GA and SVM
    Liu, Ning
    Yang, Min
    Zhang, Shibin
    [J]. PROCEEDINGS OF THE 2017 2ND INTERNATIONAL CONFERENCE ON ELECTRICAL, CONTROL AND AUTOMATION ENGINEERING (ECAE 2017), 2017, 140 : 257 - 261
  • [5] Detection of Android Applications with Malicious Behavior Based on Sparse Bayesian Learning Algorithm
    Liu, Ning
    Yang, Min
    Zhang, Hang
    Yang, Chen
    Zhao, Yang
    Gan, Jianchao
    Zhang, Shibin
    [J]. CLOUD COMPUTING AND SECURITY, PT V, 2018, 11067 : 266 - 275
  • [6] Android malicious behavior recognition and classification method based on random forest algorithm
    Ke, Dong-Xiang
    Pan, Li-Min
    Luo, Sen-Lin
    Zhang, Han-Qing
    [J]. Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science), 2019, 53 (10): : 2013 - 2023
  • [7] Detecting Malicious Android Applications from Runtime Behavior
    Lageman, Nathaniel
    Lindsey, Mark
    Glodek, William
    [J]. 2015 IEEE MILITARY COMMUNICATIONS CONFERENCE (MILCOM 2015), 2015, : 324 - 329
  • [8] Detection of malicious behavior in android apps through API calls and permission uses analysis
    Yang, Ming
    Wang, Shan
    Ling, Zhen
    Liu, Yaowen
    Ni, Zhenyu
    [J]. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2017, 29 (19):
  • [9] An analysis of widget layout attributes to support Android GUI-based testing
    Fulcini, Tommaso
    Coppola, Riccardo
    Torchiano, Marco
    Ardito, Luca
    [J]. 2023 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE TESTING, VERIFICATION AND VALIDATION WORKSHOPS, ICSTW, 2023, : 117 - 125
  • [10] A Multidimensional Detection Model of Android Malicious Applications Based on Dynamic and Static Analysis
    Zhang, Hao
    Liu, Donglan
    Liu, Xin
    Ma, Lei
    Wang, Rui
    Zhang, Fangzhe
    Sun, Lili
    Zhao, Fuhui
    [J]. PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING AND NETWORKS, VOL III, CENET 2023, 2024, 1127 : 11 - 21