  Researchers have proposed a wide range of ransomware detection and analysis
schemes. However, most of these efforts have focused on older families
targeting Windows 7/8 systems. Hence there is a critical need to develop
efficient solutions to tackle the latest threats, many of which may have
relatively fewer samples to analyze. This paper presents a machine learning(ML)
framework for early ransomware detection and attribution. The solution pursues
a data-centric approach which uses a minimalist ransomware dataset and
implements static analysis using portable executable(PE) files. Results for
several ML classifiers confirm strong performance in terms of accuracy and
zero-day threat detection.
