How hard can it be? Quantifying MITRE attack campaigns with attack trees and cATM logic

TL;DR

This paper presents a data-driven framework using attack trees and cATM logic to quantify and compare the likelihood of cyber attack campaigns from the MITRE database, aiding cybersecurity decision-making.

Contribution

It introduces a novel, automated methodology for modeling attack campaigns with attack trees and cATM logic, reducing effort and improving quantitative comparison capabilities.

Findings

Quantified likelihood of all MITRE Enterprise campaigns.

Compared likelihood of Wocao and Dream Job campaigns.

Demonstrated lighter modeling effort with the proposed approach.

Abstract

The landscape of cyber threats grows more complex by the day. Advanced Persistent Threats carry out attack campaigns - e.g. operations Dream Job, Wocao, and WannaCry - against which cybersecurity practitioners must defend. To prioritise which of these to defend against, cybersecurity experts must be equipped with the right toolbox to evaluate the most threatening ones. In particular, they would strongly benefit from (a) an estimation of the likelihood values for each attack recorded in the wild, and (b) transparently operationalising these values to compare campaigns quantitatively. Security experts could then perform transparent and accountable quantitatively-informed decisions. Here we construct such a framework: (1) quantifying the likelihood of attack campaigns via data-driven procedures on the MITRE knowledge-base, (2) introducing a methodology for automatic modelling of MITRE intelligence data, that captures any attack campaign via template attack tree models, and (3) proposing an open-source tool to perform these comparisons based on the cATM logic. Finally, we quantify the likelihood of all MITRE Enterprise campaigns, and compare the likelihood of the Wocao and Dream Job MITRE campaigns - generated with our proposed approach - against manually-built attack tree models. We demonstrate how our methodology is substantially lighter in modelling effort, and capable of capturing all the quantitative relevant data.