Advanced malware detection for android platform

• Main Author: XU, Ke
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Malware detection; Android; Security; Privacy; Machine learning; Static analytics; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/etd_coll/183; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1183&context=etd_coll
• Institution: Singapore Management University

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.