Coordinated Attack Planning in Probabilistic Attack Graphs Within a Sensor-Allocated Network.

Modern cyberattacks increasingly involve coordinated teams of adversaries, posing new challenges for detection and defense. This paper introduces a probabilistic framework for modeling such multi-agent attacks, combining Markov Decision Processes with an augmented Markov game that captures dynamic team composition under detection events. The model integrates state-dependent detection probabilities, implicit coordination via reward structures, and efficient value iteration for policy computation. Through a stochastic grid-world case study, we analyze the performance trade-offs between isolated and coordinated strategies. Our results reveal that while isolated agents perform better in simple, single-target scenarios, coordinated strategies significantly improve success rates in complex, multi-target operations by systematically managing exposure. This work provides a formal foundation and practical methodology for analyzing emergent attack behaviors in adversarial environments.