Publication Type
PhD Dissertation
Version
publishedVersion
Publication Date
6-2018
Abstract
In the first quarter of 2018, 75.66% of smartphones sales were devices running An- droid. Due to its popularity, cyber-criminals have increasingly targeted this ecosys- tem. Malware running on Android severely violates end users security and privacy, allowing many attacks such as defeating two factor authentication of mobile bank- ing applications, capturing real-time voice calls and leaking sensitive information. In this dissertation, I describe the pieces of work that I have done to effectively de- tect malware on Android platform, i.e., ICC-based malware detection system (IC- CDetector), multi-layer malware detection system (DeepRefiner), and self-evolving and scalable malware detection system (DroidEvolver) for Android platform.
Keywords
Malware detection, Android, Security, Privacy, Machine learning, Static analytics
Degree Awarded
PhD in Information Systems
Discipline
Software Engineering
Supervisor(s)
LI, Yingjiu; DENG, Huijie Robert
Publisher
Singapore Management University
City or Country
Singapore
Citation
XU, Ke.
Advanced malware detection for android platform. (2018).
Available at: https://ink.library.smu.edu.sg/etd_coll/183
Copyright Owner and License
Author
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.