Existing researches on cyber attackdefense analysis have typically adopted stochastic game theory to model the problem for solutions,but the assumption of complete rationality is used in modeling,ignoring the informat...Existing researches on cyber attackdefense analysis have typically adopted stochastic game theory to model the problem for solutions,but the assumption of complete rationality is used in modeling,ignoring the information opacity in practical attack and defense scenarios,and the model and method lack accuracy.To such problem,we investigate network defense policy methods under finite rationality constraints and propose network defense policy selection algorithm based on deep reinforcement learning.Based on graph theoretical methods,we transform the decision-making problem into a path optimization problem,and use a compression method based on service node to map the network state.On this basis,we improve the A3C algorithm and design the DefenseA3C defense policy selection algorithm with online learning capability.The experimental results show that the model and method proposed in this paper can stably converge to a better network state after training,which is faster and more stable than the original A3C algorithm.Compared with the existing typical approaches,Defense-A3C is verified its advancement.展开更多
针对三维虚拟士兵在军事仿真领域的需求,重点研究了虚拟士兵几何外形特征的快速重建,动作分析与合成,行为模型等关键技术,并基于OpenGL开发了具有较强通用性的数字化三维虚拟士兵生成与模拟开发平台(3D Soldier Pro SDK)。该平台在三维...针对三维虚拟士兵在军事仿真领域的需求,重点研究了虚拟士兵几何外形特征的快速重建,动作分析与合成,行为模型等关键技术,并基于OpenGL开发了具有较强通用性的数字化三维虚拟士兵生成与模拟开发平台(3D Soldier Pro SDK)。该平台在三维战场仿真、城市反恐作战仿真等领域中有广泛应用。展开更多
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)The Project of Science and Technology in Henan Province(No.242102211068,No.232102210078)+2 种基金The Key Field Special Project of Guangdong Province(No.2021ZDZX1098)The China University Research Innovation Fund(No.2021FNB3001,No.2022IT020)Shenzhen Science and Technology Innovation Commission Stable Support Plan(No.20231128083944001)。
文摘Existing researches on cyber attackdefense analysis have typically adopted stochastic game theory to model the problem for solutions,but the assumption of complete rationality is used in modeling,ignoring the information opacity in practical attack and defense scenarios,and the model and method lack accuracy.To such problem,we investigate network defense policy methods under finite rationality constraints and propose network defense policy selection algorithm based on deep reinforcement learning.Based on graph theoretical methods,we transform the decision-making problem into a path optimization problem,and use a compression method based on service node to map the network state.On this basis,we improve the A3C algorithm and design the DefenseA3C defense policy selection algorithm with online learning capability.The experimental results show that the model and method proposed in this paper can stably converge to a better network state after training,which is faster and more stable than the original A3C algorithm.Compared with the existing typical approaches,Defense-A3C is verified its advancement.
