Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networks
This addresses the challenge of network security for scalable and dynamic networks, but it appears incremental as it builds on existing autonomous COA techniques.
The paper tackles the problem of autonomous attack search in scalable and time-varying networks by proposing a spatio-temporal algorithm that combines an intelligent spatial search with a Monte-Carlo-based temporal approach, resulting in efficient operations for such networks.
One of the key topics in network security research is the autonomous COA (Couse-of-Action) attack search method. Traditional COA attack search methods that passively search for attacks can be difficult, especially as the network gets bigger. To address these issues, new autonomous COA techniques are being developed, and among them, an intelligent spatial algorithm is designed in this paper for efficient operations in scalable networks. On top of the spatial search, a Monte-Carlo (MC)- based temporal approach is additionally considered for taking care of time-varying network behaviors. Therefore, we propose a spatio-temporal attack COA search algorithm for scalable and time-varying networks.