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Given a large enterprise network of devices and their authentication history(e.g., device logons), how can we quantify network vulnerability to lateralattack and identify at-risk devices? We systematically address these problemsthrough D2M, the first framework that models lateral attacks on enterprisenetworks using multiple attack strategies developed with researchers,engineers, and threat hunters in the Microsoft Defender Advanced ThreatProtection group. These strategies integrate real-world adversarial actions(e.g., privilege escalation) to generate attack paths: a series of compromisedmachines. Leveraging these attack paths and a novel Monte-Carlo method, weformulate network vulnerability as a probabilistic function of the networktopology, distribution of access credentials and initial penetration point. Toidentify machines at risk to lateral attack, we propose a suite of five fastgraph mining techniques, including a novel technique called AnomalyShieldinspired by node immunization research. Using three real-world authenticationgraphs from Microsoft and Los Alamos National Laboratory (up to 223,399authentications), we report the first experimental results on networkvulnerability to lateral attack, demonstrating D2M's unique potential toempower IT admins to develop robust user access credential policies.

D2M: Dynamic Defense and Modeling of Adversarial Movement in Networks