Detecting android malware by applying classification techniques on images patterns
Manzhi Yang, Qiaoyan Wen · 2017
For the detection of unknown malicious code, most successful of the classification algorithms are based on PC files patterns, which are extracted features based on the inspected files or the files after disassembly. Nowadays, the amount of the application in Android App Market has grown fast, therefore android malware has been introduced fast into that market, too. In this paper, we represent the inspected APK files using images patterns which are unzipped from the android files and use classification algorithm to find out the malware images similarities.