Coordinated Attack Planning in Probabilistic Attack Graphs within a Sensor-Allocated Network

Romaric Mofouet

doi:10.1007/978-3-032-09315-8_8

Coordinated Attack Planning in Probabilistic Attack Graphs within a Sensor-Allocated Network

2026-01-01

0

PRE

AI

DOI：10.1007/978-3-032-09315-8_8

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

Multi-agent

Coordinated attack

Markov process

Valueiteration

Coordinated Attack Planning in Probabilistic Attack Graphs within a Sensor-Allocated Network

Abstract

Journal

Organization