Papers › SCRUTINIZER: Detecting Code Reuse in Malware via Decompilation and Machine Learning

SCRUTINIZER: Detecting Code Reuse in Malware via Decompilation and Machine Learning

9 Jul 2021International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment 2021 7archive 2025-07-28

Omid Mirzaei, Roman Vasilenko, Engin Kirda, Long Lu, Amin Kharraz

Growing numbers of advanced malware-based attacks against governments and corporations, for political, financial and scientific gains, have taken security breaches to the next level. In response to such attacks, both academia and industry have investigated techniques to model and reconstruct these attacks and to defend against them. While such efforts have been all useful in mitigating the effects of modern attacks, automated malware code reuse inspection and campaign attribution have received less attention. In this paper, we present an automated system, called SCRUTINIZER, to identify code reuse in malware via a novel machine learning-based encoding mechanism at the function-level. By creating a large knowledge base of previously observed and tagged malware campaigns, we can compare unknown samples against this knowledge base and determine how much overlap exists. SCRUTINIZER leverages an unsupervised learning approach to filter out irrelevant functions before code reuse detection. It provides two valuable capabilities. First, it identifies ties between an unknown sample and those malware specimens that are known to be used by a specific campaign. Second, it inspects if specific tools or functionalities are used by a campaign. Using SCRUTINIZER, we were able to identify 12 samples that were previously unknown to us and that we were able to correctly assign to well-known APT campaigns.

Paper PDF Code

Code

OMirzaei/SCRUTINIZER mentioned in paper report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

BIG-bench Machine Learning Code Reuse Detection Malware Analysis

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections