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High Stakes, Low Certainty: Evaluating the Efficacy of High-Level Indicators of Compromise in Ransomware Attribution: Artifacts

Name: High Stakes, Low Certainty: Evaluating the Efficacy of High-Level Indicators of Compromise in Ransomware Attribution: Artifacts
Keywords: Computer security

Research datakeyboard_double_arrow_right Dataset 24 Jan 2025 English Publisher:Zenodo

Authors: van der Horst, Max; Kho, Ricky; Gadyatskaya, Olga; Mollema, Michel; van Eeten, Michel; Zhauniarovich, Yury;

doi: 10.5281/zenodo.14732550 , 10.5281/zenodo.14732551

High Stakes, Low Certainty: Evaluating the Efficacy of High-Level Indicators of Compromise in Ransomware Attribution: Artifacts

- Summary
- Subjects
- Metrics

Abstract

This dataset is part of the publication "High Stakes, Low Certainty: Evaluating the Efficacy of High-Level Indicators of Compromise in Ransomware Attribution", to appear in USENIX Security 2025. It contains the artifacts required for evaluation as well as the appendices from the paper. The artifacts required for evaluation are: The codebook resulting from the interviews The data representing the studied RTA groups The code written to analyze these RTA groups Additional information is included in the README files.

Related Organizations

Delft University of Technology
Netherlands
Leiden University
Netherlands

Keywords

Computer security

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

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