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Malware is a significant threat to the security of computer systems andnetworks which requires sophisticated techniques to analyze the behavior andfunctionality for detection. Traditional signature-based malware detectionmethods have become ineffective in detecting new and unknown malware due totheir rapid evolution. One of the most promising techniques that can overcomethe limitations of signature-based detection is to use control flow graphs(CFGs). CFGs leverage the structural information of a program to represent thepossible paths of execution as a graph, where nodes represent instructions andedges represent control flow dependencies. Machine learning (ML) algorithms arebeing used to extract these features from CFGs and classify them as maliciousor benign. In this survey, we aim to review some state-of-the-art methods formalware detection through CFGs using ML, focusing on the different ways ofextracting, representing, and classifying. Specifically, we present acomprehensive overview of different types of CFG features that have been usedas well as different ML algorithms that have been applied to CFG-based malwaredetection. We provide an in-depth analysis of the challenges and limitations ofthese approaches, as well as suggest potential solutions to address some openproblems and promising future directions for research in this field.

Survey of Malware Analysis through Control Flow Graph using Machine Learning