Automatically Detecting Malicious Sensitive Data Usage in Android Applications

被引:0
|
作者
Yan, Hongbing [1 ]
Xiong, Yan [1 ]
Huang, Wenchao [1 ]
Huang, Jianmeng [1 ]
Meng, Zhaoyi [1 ]
机构
[1] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei, Anhui, Peoples R China
关键词
D O I
10.1109/BIGCOM.2018.00023
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Android devices have increased rapidly in recent years. Because sensitive data of users can bring huge profits, there are so many malicious Android applications (apps) which aim at users' sensitive data in Android markets. Malicious apps may collect sensitive data of users, such as phone number, location, contact information, and send them to advertisers or attackers. To prevent malicious apps from stealing user information, a simple solution is not to grant corresponding permissions to apps. But if we don't give corresponding permissions, the apps may exit directly. This affects the normal use of apps. In order to solve the above problems, we design a system which uses machine-learning technology to detect malicious behaviours. Our system is based on the observation that apps in the same category usually use sensitive data in the same or similar way. The system implements automatic detection of malicious behaviours. The true positive rate of our system can be over 90% and the false positive rate can be below 8%.
引用
收藏
页码:102 / 107
页数:6
相关论文
共 50 条
  • [21] Detection of Malicious Applications on Android OS
    Di Cerbo, Francesco
    Girardello, Andrea
    Michahelles, Florian
    Voronkova, Svetlana
    COMPUTATIONAL FORENSICS, 2011, 6540 : 138 - +
  • [22] Detecting Android application malicious behaviors based on the analysis of control flows and data flows
    Zegzhda, Peter
    Zegzhda, Dmitry
    Pavlenko, Evgeny
    Dremov, Andrew
    SIN'17: PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON SECURITY OF INFORMATION AND NETWORKS, 2017, : 280 - 283
  • [23] Detecting Malicious Facebook Applications
    Rahman, Sazzadur
    Huang, Ting-Kai
    Madhyastha, Harsha V.
    Faloutsos, Michalis
    IEEE-ACM TRANSACTIONS ON NETWORKING, 2016, 24 (02) : 773 - 787
  • [24] Detecting sensitive data leakage via inter-applications on Android using a hybrid analysis technique
    Nguyen Tan Cam
    Van-Hau Pham
    Tuan Nguyen
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2019, 22 (Suppl 1): : 1055 - 1064
  • [25] Detecting sensitive data leakage via inter-applications on Android using a hybrid analysis technique
    Nguyen Tan Cam
    Van-Hau Pham
    Tuan Nguyen
    Cluster Computing, 2019, 22 : 1055 - 1064
  • [26] Detecting Malicious Android Game Applications on Third-Party Stores Using Machine Learning
    Sanamontre, Thanaporn
    Visoottiviseth, Vasaka
    Ragkhitwetsagul, Chaiyong
    ADVANCED INFORMATION NETWORKING AND APPLICATIONS, VOL 4, AINA 2024, 2024, 202 : 238 - 251
  • [27] Guarding Sensitive Sensor Data against Malicious Mobile Applications
    Claiborne, Cynthia
    Dantu, Ram
    Ncube, Cathy
    2020 SIXTH INTERNATIONAL CONFERENCE ON MOBILE AND SECURE SERVICES (MOBISECSERV)), 2020,
  • [28] AppScalpel: Combining static analysis and outlier detection to identify and prune undesirable usage of sensitive data in Android applications
    Meng, Zhaoyi
    Xiong, Yan
    Huang, Wenchao
    Qin, Lei
    Jin, Xin
    Yan, Hongbing
    NEUROCOMPUTING, 2019, 341 : 10 - 25
  • [29] AndroCom: A Real-World Android Applications' Vulnerability Dataset to Assist with Automatically Detecting Vulnerabilities
    Arikan, Kaya Emre
    Yilmaz, Ercan Nurcan
    APPLIED SCIENCES-BASEL, 2025, 15 (05):
  • [30] An Analysis on Sensitive Data Passive Leakage in Android Applications
    Yang, Tianchang
    Cui, Haoliang
    Niu, Shaozhang
    Zhang, Peng
    2015 IEEE 16TH INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY (ICCT), 2015, : 125 - 131