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Aviator: A MITRE Emulation Plan-Derived Living Dataset for Advanced Persistent Threat Detection and Investigation

Liu, Qi ORCID iD icon; Bao, Kaibin ORCID iD icon 1; Hagenmeyer, Veit ORCID iD icon 1
1 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)

Abstract:

With the growing trend for developing new detection and investigation systems for Advanced Persistent Threat (APT), the urgent issue of lacking sound and authentic datasets becomes more visible. New datasets for research on APT detection and investigation have been released over the past few years in an accelerated manner. Yet, our examination of the existing datasets yields the finding that the gap between these datasets’ attack scenarios and real-world APT attacks is significant. Recognizing the flaws of prior datasets particularly in terms of attack scenario complexity and authenticity, we develop a novel sound dataset called AVIATOR, which is backed by MITRE emulation plans. The well-known organization MITRE has released nearly a dozen emulation plans, which closely reproduce APT groups’ realworld attack campaigns observed in the past. However MITRE has not published any datasets. Thus, we resort to stringently implementing these emulation plans. Further, we extend these emulation plans to include an industrial control system and attack steps on it, mimicking APT groups most known for their attacks against critical infrastructures in the past. ... mehr

Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 15.12.2024
Sprache Englisch
Identifikator ISBN: 979-8-3503-6248-0
KITopen-ID: 1000178581
HGF-Programm 37.12.01 (POF IV, LK 01) Digitalization & System Technology for Flexibility Solutions
Weitere HGF-Programme 46.23.02 (POF IV, LK 01) Engineering Security for Energy Systems
Erschienen in 2024 IEEE International Conference on Big Data (BigData)
Veranstaltung IEEE International Conference on Big Data (IEEE Big Data 2024), Washington, DC, USA, 15.12.2024 – 18.12.2024
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 5610–5619
Schlagwörter Dataset, Advanced Persistent Threat emulation, threat intelligence, auditing, logging, data provenance analysis
Nachgewiesen in Dimensions
Scopus
OpenAlex
Globale Ziele für nachhaltige Entwicklung Ziel 9 – Industrie, Innovation und InfrastrukturZiel 13 – Maßnahmen zum Klimaschutz

Originalveröffentlichung
DOI: 10.1109/BigData62323.2024.10826006
Seitenaufrufe: 54
seit 31.01.2025
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