Malware is a significant threat to the security of computer systems and
networks which requires sophisticated techniques to analyze the behavior and
functionality for detection. Traditional signature-based malware detection
methods have become ineffective in detecting new and unknown malware due to
their rapid evolution. One of the most promising techniques that can overcome
the limitations of signature-based detection is to use control flow graphs
(CFGs). CFGs leverage the structural information of a program to represent the
possible paths of execution as a graph, where nodes represent instructions and
edges represent control flow dependencies. Machine learning (ML) algorithms are
being used to extract these features from CFGs and classify them as malicious
or benign. In this survey, we aim to review some state-of-the-art methods for
malware detection through CFGs using ML, focusing on the different ways of
extracting, representing, and classifying. Specifically, we present a
comprehensive overview of different types of CFG features that have been used
as well as different ML algorithms that have been applied to CFG-based malware
detection. We provide an in-depth analysis of the challenges and limitations of
these approaches, as well as suggest potential solutions to address some open
problems and promising future directions for research in this field.

通过对控制流图的特征提取和分类，以机器学习为基础的方法成为检测恶意软件的有前途的技术之一。本篇论文综述了不同的 CFG 特征提取方式、不同的 ML 算法以及存在的挑战和限制，同时提出了一些解决方案和未来研究的方向。