针对企业网络的网络攻击利用基础设施、服务与应用之间复杂的依赖关系，这给仅关注攻击路径或网络拓扑的传统分析方法带来了挑战。本研究提出一种基于网络中影响传播的新型概率多层建模框架，将攻击图与通信网络拓扑相融合，从而支持服务中心化的网络攻击影响分析。该方法同时刻画漏洞可利用性与网络连通性，使我们能够评估攻击在网络互连服务间传播的可能性及其累积影响。通过将标准漏洞指标（如CVSS）与网络层级的连通性概率相结合，该框架提供了对网络攻击动态过程的一致性刻画。我们在一个真实的企业网络案例中验证了该方法，结果表明其能够识别显著影响攻击结果的关键节点、漏洞及服务依赖关系。研究发现表明，融合网络图与攻击图视角可为风险评估与缓解规划提供更具操作性的洞见，从而推进复杂网络化环境中网络攻击的分析能力。

Cyberattacks on enterprise networks exploit complex dependencies among infrastructure, services, and applications, which challenge traditional analysis methods that focus on attack paths or network topology in isolation. In this study, we introduce a novel probabilistic multilayer modelling framework, based on influence propagation in networks, that integrates attack graphs with the communication network topology, enabling a service-centric impact analysis of cyberattacks. Our method captures both the vulnerability exploitability and network connectivity, allowing us to assess the likelihood of attack propagation and cumulative impacts across interconnected services. By integrating standard vulnerability metrics (such as CVSS) with the network-level connectivity probabilities, the framework provides a cohesive view of the dynamics of cyberattacks. We validate this approach using a realistic case study of an enterprise network, demonstrating its ability to determine critical nodes, vulnerabilities, and service dependencies that significantly influence attack outcomes. Our findings show that integrating network and attack graph perspectives offers more actionable insights into risk assessment and mitigation planning, advancing the analysis of cyberattacks in complex networked environments.