Biblio
Filters: Author is Kim, Joongheon [Clear All Filters]
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2022. Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networks. 2022 13th International Conference on Information and Communication Technology Convergence (ICTC). :1581—1584.
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.
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2019. Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms. 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks – Supplemental Volume (DSN-S). :3–4.
This paper proposes a distributed deep learning framework for privacy-preserving medical data training. In order to avoid patients' data leakage in medical platforms, the hidden layers in the deep learning framework are separated and where the first layer is kept in platform and others layers are kept in a centralized server. Whereas keeping the original patients' data in local platforms maintain their privacy, utilizing the server for subsequent layers improves learning performance by using all data from each platform during training.