Abstract: Attack path discovery is a critical task for cyber asset security assessment.The existing artificial intelligence-based planning for attack path discovery method is favored by security practitioners due to its rich modeling language and complete planning algorithm,but its scalability problem cannot be ignored.For that reason,a hierarchical task network-based attack path method is proposed.Specifically,concerning the scalability problem,the proposed method is decomposed into the following three stages:first and foremost,focusing on undesirable attack path generation performance caused by expanding network scale,a multi-level K-way partitioning algorithm is introduced into the target topology; subsequently,focusing on the difficulty of domain problem description caused by complex discovery tasks,an attack path-oriented hierarchical task network is constructed with the combination of expert experiments; and finally,focusing on low attack path updating efficiency caused by demands on what-if security analysis,a maintenance scheme is designed for local information changes of assets.Experimental results show that the proposed method is suitable for attack path discovery in large-scale network which has an advantage over efficiency and scalability.

Key words: Artificial intelligence planning, Hierarchical task network, Multi-level K-way partitioning algorithm, Attack path discovery, Attack path extension