Study of incremental fuzzy clustering for malware analysis

• Main Author: Li, Xinyi.
• Other Authors: Chen Lihui
• Format: Final Year Project
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/54474
• Institution: Nanyang Technological University

Internet is playing a important role in modern lifestyle. On the other hand, it brings the problem of cyber security. Malicious software, also known as Malware, is a major threat to cyber security. One malware may have large amount and various types of variants. It takes human analysts some time to respond using classic static analysis technique. Therefore dynamic malware analysis techniques are proposed to improve the efficiency of identifying new unknown malware and variants of known malware. In this project, three clustering algorithms based on Fuzzy C-means are implemented and applied in malware data analysis. They are proved to be work well on medium size dataset with multi-attributes. For large dataset such as malware datasets, they cannot function as expected. Only two out of three new algorithms implemented can be successfully applied for handling malware datasets.