Abstract
We discuss the capability of a new feature set for malware detection based on potential component leaks (PCLs). PCLs are defined as sensitive data-flows that involve Android inter-component communications. We show that PCLs are common in Android apps and that malicious applications indeed manipulate significantly more PCLs than benign apps. Then, we evaluate a machine learning-based approach relying on PCLs. Experimental validations show high performance for identifying malware, demonstrating that PCLs can be used for discriminating malicious apps from benign apps.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2015 IEEE International Conference on Software Quality, Reliability and Security, QRS 2015 |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 195-200 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781467379892 |
| DOIs | |
| Publication status | Published - 21 Sep 2015 |
| Externally published | Yes |
| Event | IEEE International Conference on Software Quality, Reliability and Security, QRS 2015 - Vancouver, Canada Duration: 3 Aug 2015 → 5 Aug 2015 |
Conference
| Conference | IEEE International Conference on Software Quality, Reliability and Security, QRS 2015 |
|---|---|
| Country | Canada |
| City | Vancouver |
| Period | 3/08/15 → 5/08/15 |
Cite this
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Potential Component Leaks in Android Apps : An Investigation into a New Feature Set for Malware Detection. / Li, Li; Allix, Kevin; Li, Daoyuan; Bartel, Alexandre; Bissyandé, Tegawendé F.; Klein, Jacques.
Proceedings - 2015 IEEE International Conference on Software Quality, Reliability and Security, QRS 2015. IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 195-200 7272932.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - Potential Component Leaks in Android Apps
T2 - An Investigation into a New Feature Set for Malware Detection
AU - Li, Li
AU - Allix, Kevin
AU - Li, Daoyuan
AU - Bartel, Alexandre
AU - Bissyandé, Tegawendé F.
AU - Klein, Jacques
PY - 2015/9/21
Y1 - 2015/9/21
N2 - We discuss the capability of a new feature set for malware detection based on potential component leaks (PCLs). PCLs are defined as sensitive data-flows that involve Android inter-component communications. We show that PCLs are common in Android apps and that malicious applications indeed manipulate significantly more PCLs than benign apps. Then, we evaluate a machine learning-based approach relying on PCLs. Experimental validations show high performance for identifying malware, demonstrating that PCLs can be used for discriminating malicious apps from benign apps.
AB - We discuss the capability of a new feature set for malware detection based on potential component leaks (PCLs). PCLs are defined as sensitive data-flows that involve Android inter-component communications. We show that PCLs are common in Android apps and that malicious applications indeed manipulate significantly more PCLs than benign apps. Then, we evaluate a machine learning-based approach relying on PCLs. Experimental validations show high performance for identifying malware, demonstrating that PCLs can be used for discriminating malicious apps from benign apps.
UR - http://www.scopus.com/inward/record.url?scp=84962109836&partnerID=8YFLogxK
U2 - 10.1109/QRS.2015.36
DO - 10.1109/QRS.2015.36
M3 - Conference Paper
SP - 195
EP - 200
BT - Proceedings - 2015 IEEE International Conference on Software Quality, Reliability and Security, QRS 2015
PB - IEEE, Institute of Electrical and Electronics Engineers
ER -