Abstract
In recent years, we witnessed a drastic increase of ransomware, especially on popular mobile platforms including Android. Ransomware extorts victims for a sum of money by taking control of their devices or files. In light of their rapid growth, there is a pressing need to develop effective countermeasure solutions. However, the research community is still constrained by the lack of a comprehensive dataset, and there exists no insightful understanding of mobile ransomware in the wild. In this paper, we focus on the Android platform and aim to characterize existing Android ransomware. Specifically, we have managed to collect 2,721 ransomware samples that cover the majority of existing Android ransomware families. Based on these samples, we systematically characterize them from several aspects, including timeline and malicious features. In addition, the detection results of existing anti-virus tools are rather disappointing, which clearly calls for customized anti-mobile-ransomware solutions. To detect ransomware that extorts users by encrypting data, we propose a novel real-time detection system, called RansomProber. By analyzing the UI widgets of related activities and the coordinates of users’ finger movements, RansomProber can infer whether the file encryption operations are initiated by users. The experimental results show that RansomProber can effectively detect encrypting ransomware with high accuracy and acceptable runtime performance.
| Original language | English (US) |
|---|---|
| Journal | IEEE Transactions on Information Forensics and Security |
| DOIs | |
| State | Accepted/In press - Dec 27 2017 |
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Keywords
- Android
- Ransomware
- Real-time Detection
- UI Indicator
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Computer Networks and Communications
Cite this
Uncovering the Face of Android Ransomware : Characterization and Real-time Detection. / Chen, Jing; Wang, Chiheng; Zhao, Ziming; Chen, Kai; Du, Ruiying; Ahn, Gail-Joon.
In: IEEE Transactions on Information Forensics and Security, 27.12.2017.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Uncovering the Face of Android Ransomware
T2 - Characterization and Real-time Detection
AU - Chen, Jing
AU - Wang, Chiheng
AU - Zhao, Ziming
AU - Chen, Kai
AU - Du, Ruiying
AU - Ahn, Gail-Joon
PY - 2017/12/27
Y1 - 2017/12/27
N2 - In recent years, we witnessed a drastic increase of ransomware, especially on popular mobile platforms including Android. Ransomware extorts victims for a sum of money by taking control of their devices or files. In light of their rapid growth, there is a pressing need to develop effective countermeasure solutions. However, the research community is still constrained by the lack of a comprehensive dataset, and there exists no insightful understanding of mobile ransomware in the wild. In this paper, we focus on the Android platform and aim to characterize existing Android ransomware. Specifically, we have managed to collect 2,721 ransomware samples that cover the majority of existing Android ransomware families. Based on these samples, we systematically characterize them from several aspects, including timeline and malicious features. In addition, the detection results of existing anti-virus tools are rather disappointing, which clearly calls for customized anti-mobile-ransomware solutions. To detect ransomware that extorts users by encrypting data, we propose a novel real-time detection system, called RansomProber. By analyzing the UI widgets of related activities and the coordinates of users’ finger movements, RansomProber can infer whether the file encryption operations are initiated by users. The experimental results show that RansomProber can effectively detect encrypting ransomware with high accuracy and acceptable runtime performance.
AB - In recent years, we witnessed a drastic increase of ransomware, especially on popular mobile platforms including Android. Ransomware extorts victims for a sum of money by taking control of their devices or files. In light of their rapid growth, there is a pressing need to develop effective countermeasure solutions. However, the research community is still constrained by the lack of a comprehensive dataset, and there exists no insightful understanding of mobile ransomware in the wild. In this paper, we focus on the Android platform and aim to characterize existing Android ransomware. Specifically, we have managed to collect 2,721 ransomware samples that cover the majority of existing Android ransomware families. Based on these samples, we systematically characterize them from several aspects, including timeline and malicious features. In addition, the detection results of existing anti-virus tools are rather disappointing, which clearly calls for customized anti-mobile-ransomware solutions. To detect ransomware that extorts users by encrypting data, we propose a novel real-time detection system, called RansomProber. By analyzing the UI widgets of related activities and the coordinates of users’ finger movements, RansomProber can infer whether the file encryption operations are initiated by users. The experimental results show that RansomProber can effectively detect encrypting ransomware with high accuracy and acceptable runtime performance.
KW - Android
KW - Ransomware
KW - Real-time Detection
KW - UI Indicator
UR - http://www.scopus.com/inward/record.url?scp=85040065978&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040065978&partnerID=8YFLogxK
U2 - 10.1109/TIFS.2017.2787905
DO - 10.1109/TIFS.2017.2787905
M3 - Article
JO - IEEE Transactions on Information Forensics and Security
JF - IEEE Transactions on Information Forensics and Security
SN - 1556-6013
ER -