How hard can it be? Quantifying MITRE attack campaigns with attack trees and cATM logic
<|reference_start|>How hard can it be? Quantifying MITRE attack campaigns with attack trees and cATM logic: The landscape of cyber threats grows more complex by the day. Advanced Persistent Threats carry out systematic attack campaigns against which cybersecurity practitioners must defend. Examples of such organized attacks are operations Dream Job, Wocao, WannaCry or the SolarWinds Compromise. To evaluate which risks are most threatening, and which campaigns to prioritize against when defending, cybersecurity experts must be equipped with the right toolbox. In particular, they must be able to (a) obtain likelihood values for each attack campaign recorded in the wild and (b) reliably and transparently operationalize these values to carry out quantitative comparisons among campaigns. This will allow security experts to perform quantitatively-informed decision making that is transparent and accountable. In this paper we construct such a framework by: (1) quantifying the likelihood of attack campaigns via data-driven procedures on the MITRE knowledge base and (2) introducing a methodology for automatic modelling of MITRE intelligence data: this is complete in the sense that it captures any attack campaign via template attack tree models. (3) We further propose a computational framework to carry out this comparisons based on the cATM formal logic, and implement this into an open-source Python tool. Finally, we validate our approach by quantifying the likelihood of all MITRE campaigns, and comparing the likelihood of the Wocao and Dream Job MITRE campaigns -- generated with our proposed approach -- against "ad hoc" traditionally-built attack tree models, demonstrating how our methodology is substantially lighter in modelling effort, and still capable of capturing all the quantitative relevant data.<|reference_end|>
@article{nicoletti2024how,
    title={How hard can it be? Quantifying MITRE attack campaigns with attack trees
  and cATM logic},
    author={Stefano M. Nicoletti, Milan Lopuha"a-Zwakenberg, Mari"elle
  Stoelinga, Fabio Massacci, Carlos E. Budde},
    journal={arXiv preprint arXiv:2410.06692},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06692},
    primaryClass={cs.CR cs.LO}
}