Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networks

TL;DR

This paper introduces a novel spatio-temporal attack search algorithm that combines spatial and Monte Carlo-based temporal methods to efficiently identify attack strategies in large, dynamic networks.

Contribution

It presents an integrated spatio-temporal approach that enhances autonomous attack search in scalable, time-varying networks, addressing limitations of traditional methods.

Findings

Efficient attack search in large networks.

Effective handling of time-varying network behaviors.

Improved scalability of attack detection methods.

Abstract

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.