SCRUTINIZER: Detecting Code Reuse in Malware via Decompilation and Machine Learning
VERSION:
Version (by release date): 2021-05-08
DEVELOPER INFORMATION:
Name: Omid Mirzaei
Laboratory: Systems Security Lab (SecLab)
University: Northeastern University
Website: https://0m1d.com/software/SCRUTINIZER
PUBLICATION:
SCRUTINIZER: Detecting Code Reuse in Malware via Decompilation and Machine Learning
O. Mirzaei, R. Vasilenko, E. Kirda, L. Lu, A. Kharraz
International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA), Online (July 2021)
ACCESS INSTRUCTIONS:
Due to the sensitivity of this research area, we request that applicants:
- Do not redistribute the code, data and other information without our consent.
- Do not make a commercial usage of our code and data.
Please, send an email to omid.[lastname of the first author]@gmail.com with this title: “Access to SCRUTINIZER”.
ACKNOWLEDGEMENT:
This work was partially-supported by National Science Foundation (NSF) under grant CNS-1703454, and the Office of Naval Research (ONR) under the “In Situ Malware” project. This work was also partially-supported by Secure Business Austria.