Biblio

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.

Deepfake detection techniques have been widely studied to resolve security issues. However, existing techniques mainly focused on RGB channel-based analysis, which still shows incomplete detection accuracy. In this paper, we validate the performance of Gray channel-based deepfake detection. To compare RGB channel-based analysis and Gray channel-based analysis in deepfake detection, we quantitatively measured the performance by using popular CNN models, deepfake datasets, and evaluation indicators. Our experimental results confirm that Gray channel-based deepfake detection outperforms RGB channel-based deepfake detection in terms of accuracy and analysis time.