Reference : Dissecting Android Cryptocurrency Miners
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/45293
Dissecting Android Cryptocurrency Miners
English
Dashevskyi, Stanislav mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Zhauniarovich, Yury mailto []
Gadyatskaya, Olga mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Pilgun, Aleksandr mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PI Mauw >]
Ouhssain, Hamza mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Mar-2020
CODASPY '20: Tenth ACM Conference on Data and Application Security and Privacy, New Orleans LA USA, March 2020
ACM
191–202
Yes
No
International
978-1-4503-7107-0
New York
NY
Tenth ACM Conference on Data and Application Security and Privacy
from 16-03-2020 to 18-03-2020
[en] Android ; cryptocurrency ; mining
[en] Cryptojacking applications pose a serious threat to mobile devices.
Due to the extensive computations, they deplete the battery fast
and can even damage the device. In this work we make a step
towards combating this threat. We collected and manually verified
a large dataset of Android mining apps. In this paper, we analyze
the gathered miners and identify how they work, what are the most
popular libraries and APIs used to facilitate their development,
and what static features are typical for this class of applications.
Further, we analyzed our dataset using VirusTotal. The majority
of our samples is considered malicious by at least one VirusTotal
scanner, but 16 apps are not detected by any engine; and at least 5
apks were not seen previously by the service.
Mining code could be obfuscated or fetched at runtime, and there
are many confusing miner-related apps that actually do not mine.
Thus, static features alone are not sufficient for miner detection.We
have collected a feature set of dynamic metrics both for miners and
unrelated benign apps, and built a machine learning-based tool for
dynamic detection. Our BrenntDroid tool is able to detect miners
with 95% of accuracy on our dataset.
Combatting Context-Sensitive Mobile Malware C15/IS/10404933/COMMA
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/45293
10.1145/3374664.3375724
https://standash.github.io/android-miners-dataset/
FnR ; FNR11289380 > Aleksandr Pilgun > > Systematically Exploring Semantic App Models for Android > 15/11/2016 > 14/11/2020 > 2016

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