机器学习辅助静态恶意软件分析：综述与展望
Machine Learning Aided Static Malware Analysis: A Survey and Tutorial
摘要：
恶意软件分析和检测技术在过去十年中一直在发展，反映了不同恶意软件技术的发展，以逃避基于网络和基于主机的安全保护。恶意软件种类和数量的快速增长使得取证调查人员很难及时做出响应。因此，机器学习（ML）辅助恶意软件分析成为自动化静态和动态恶意软件调查的不同方面的必要条件。我们相信，机器学习辅助的静态分析可以作为技术网络威胁情报（CTI）的方法论方法，而不是之前已经深入研究的消耗资源的动态恶意软件分析。在本文中，我们通过对不同的机器学习方法进行深入调查，对32位恶意可移植可执行文件（PE 32）Windows文件的静态特征进行分类，并开发分类法，以更好地理解这些技术，从而解决了这一研究空白。之后，我们提供了一个教程，介绍如何利用不同的机器学习技术来提取和分析PE二进制文件的各种静态特征，并评估这些技术的准确性和实用性。最后，通过对常用数据的实验研究，验证了该方法的准确性和复杂性。本文的研究为今后机器学习辅助恶意软件取证的跨学科研究提供了一个平台。
英文摘要：
Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections. The fast growth in variety and number of malware species made it very difficult for forensics investigators to provide an on time response. Therefore, Machine Learning (ML) aided malware analysis became a necessity to automate different aspects of static and dynamic malware investigation. We believe that machine learning aided static analysis can be used as a methodological approach in technical Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware analysis that has been thoroughly studied before. In this paper, we address this research gap by conducting an in-depth survey of different machine learning methods for classification of static characteristics of 32-bit malicious Portable Executable (PE32) Windows files and develop taxonomy for better understanding of these techniques. Afterwards, we offer a tutorial on how different machine learning techniques can be utilized in extraction and analysis of a variety of static characteristic of PE binaries and evaluate accuracy and practical generalization of these techniques. Finally, the results of experimental study of all the method using common data was given to demonstrate the accuracy and complexity. This paper may serve as a stepping stone for future researchers in cross-disciplinary field of machine learning aided malware forensics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
