Spatio-Temporal Attack Course-of-Action (COA) Search Learning for
Scalable and Time-Varying Networks
- URL: http://arxiv.org/abs/2209.00862v1
- Date: Fri, 2 Sep 2022 07:45:40 GMT
- Title: Spatio-Temporal Attack Course-of-Action (COA) Search Learning for
Scalable and Time-Varying Networks
- Authors: Haemin Lee, Seok Bin Son, Won Joon Yun, Joongheon Kim, Soyi Jung, and
Dong Hwa Kim
- Abstract summary: One of the key topics in network security research is the autonomous COA attack search method.
New autonomous COA techniques are being developed, and among them, an intelligent spatial algorithm is designed in this paper.
We propose atemporal attack COA search algorithm for scalable and time-varying networks.
- Score: 9.431571135358649
- License: http://creativecommons.org/licenses/by-sa/4.0/
- 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.
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