MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis
作者： Martina Lindorfer , Matthias Neugschwandtner , Christian Platzer , None
关键词: Android (operating system) 、 Mobile telephony 、 Machine learning 、 Malware analysis 、 Computer security 、 False positive paradox 、 Malware 、 Static analysis 、 Computer science 、 Android malware 、 Artificial intelligence
摘要: Android dominates the smartphone operating system market and consequently has attracted attention of malware authors researchers alike. Despite considerable number proposed analysis systems, comprehensive practical solutions are scarce often short-lived. Systems relying on static alone struggle with increasingly popular obfuscation dynamic code loading techniques, while purely systems prone to evasion. We present MARVIN, a that combines which leverages machine learning techniques assess risk associated unknown apps in form malice score. MARVIN performs analysis, both off-device, represent properties behavioral aspects an app through rich feature set. In our evaluation largest classification data set date, comprised over 135,000 15,000 samples, correctly classifies 98.24% malicious less than 0.04% false positives. further estimate necessary retraining interval maintain detection performance demonstrate long-term practicality approach.
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