A Comparative Study of Android Malware Behavior in Different Contexts

被引:1
|
作者
Boileau, Catherine [1 ]
Gagnon, Francois [2 ]
Poisson, Jeremie [2 ]
Frenette, Simon [2 ]
Mejri, Mohamed [1 ]
机构
[1] Univ Laval, Quebec City, PQ, Canada
[2] Cegep St Foy, CybersecLab, Quebec City, PQ, Canada
来源
DCNET: PROCEEDINGS OF THE 13TH INTERNATIONAL JOINT CONFERENCE ON E-BUSINESS AND TELECOMMUNICATIONS - VOL. 1 | 2016年
关键词
Dynamic Malware Analysis; Android; Sandboxing;
D O I
10.5220/0005997300470054
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
One of the numerous ways of addressing the Android malware threat is to run malicious applications in a sandbox environment while monitoring metrics. However, dynamic malware analysis is usually concerned with a one-time execution of an application, and information about behaviour in different environments is lacking in the literature. We fill this gap with a fuzzy-like approach to the problem: by running the same malware multiple times in different environments, we gain insight on the malware behaviour and his peculiarities. To implement this approach, we leverage a client-server sandbox to run experiments, based on a common suit of actions. Scenarios are executed multiple times on a malware sample, each time with a different parameter, and results are compared to determine variation in observed behaviour. In our current experiment, variation was introduced by different levels of simulation, allowing us to compare metrics such as failure rate, data leakages, sending of SMS, and the number of HTTP and DNS requests. We find the behaviour is different for data leakages, which require no simulation to leak information, while all results for other metrics were higher when simulation was used in experiments. We expect that a fuzzing approach with others parameters will further our understanding of malware behaviour, particularly for malware bound to such parameters.
引用
收藏
页码:47 / 54
页数:8
相关论文
共 50 条
  • [1] A Comparative Analysis of Android Malware
    Chavan, Neeraj
    Di Troia, Fabio
    Stamp, Mark
    PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS SECURITY AND PRIVACY (ICISSP), 2019, : 664 - 673
  • [2] Comparative Analysis of Android Malware Detection Techniques
    Painter, Nishant
    Kadhiwala, Bintu
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA ENGINEERING AND COMMUNICATION TECHNOLOGY, ICDECT 2016, VOL 2, 2017, 469 : 131 - 139
  • [3] Classifying Android Malware with Dynamic Behavior Dependency Graphs
    Lin, Zimin
    Wang, Rui
    Jia, Xiaoqi
    Zhang, Shengzhi
    Wu, ChuanKun
    2016 IEEE TRUSTCOM/BIGDATASE/ISPA, 2016, : 378 - 385
  • [4] On Behavior-based Detection of Malware on Android Platform
    Yu, Wei
    Zhang, Hanlin
    Ge, Linqiang
    Hardy, Rommie
    2013 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2013, : 814 - 819
  • [5] VizMal: A Visualization Tool for Analyzing the Behavior of Android Malware
    Bacci, Alessandro
    Martinelli, Fabio
    Medvet, Eric
    Mercaldo, Francesco
    ICISSP: PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS SECURITY AND PRIVACY, 2018, : 517 - 525
  • [6] Comparative Analysis of Different Feature Ranking Techniques in Data Mining-Based Android Malware Detection
    Bhattacharya, Abhishek
    Goswami, Radha Tamal
    PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON FRONTIERS IN INTELLIGENT COMPUTING: THEORY AND APPLICATIONS, FICTA 2016, VOL 1, 2017, 515 : 39 - 49
  • [7] Android malware detection based on static behavior feature analysis
    Chen C.
    Liu Y.
    Shen B.
    Cheng J.-J.
    Journal of Computers (Taiwan), 2018, 29 (06) : 243 - 253
  • [8] Shikra: A behavior-based Android malware detection framework
    Ma Zhao-hui
    Chen Zi-hao
    Wang Xin-ming
    Nic Rui-hua
    Zhao Gan-sen
    Wu Jie-chao
    Ren Xue-qi
    2017 INTERNATIONAL CONFERENCE ON GREEN INFORMATICS (ICGI), 2017, : 175 - 184
  • [9] Research on Android Malware Detection and Interception Based on Behavior Monitoring
    PENG Guojun1
    2. School of Computer
    WuhanUniversityJournalofNaturalSciences, 2012, 17 (05) : 421 - 427
  • [10] Deep Learning based Malware Detection for Android Systems: A Comparative Analysis
    Bayazit, Esra Calik
    Sahingoz, Ozgur Koray
    Dogan, Buket
    TEHNICKI VJESNIK-TECHNICAL GAZETTE, 2023, 30 (03): : 787 - 796