A Study on Static PE Malware Type Classification Using Machine Learning Techniques

• Main Authors: Shao-HuaiZhang, 張少懷
• Other Authors: Chu-Sing Yang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/w64t7t

碩士 === 國立成功大學 === 電腦與通信工程研究所 === 107 === This work aims to build an efficient, reliable and practical static malware classification system based on PE format files for Windows platform using machine learning techniques. With static analysis, feature extraction and anomaly detection can be done without executing the binary sample. With the large-scale dataset, the trained model can get more knowledge and perform better in practice. After comparing a variety of machine learning models, the best one are chosen as the final classifier in this work. Different from previous works which predict whether malicious or non-malicious, this work aims to predict not only whether malicious or not but also which type of malware it is. With this advanced information about malware type, the user can estimate the risk or damage such a malware may bring. Apart from malware type prediction, this work can produce the probability of all possible malware types. This makes our work more valuable in practice.