Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

• Published in: High-Confidence Computing Vol. 4; no. 2; p. 100145
• Main Authors: Wang, Huan, Tang, Yunlong, Wang, Yan, Wei, Ning, Deng, Junyi, Bin, Zhiyan, Li, Weilong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2024; Elsevier
• Subjects: Active defense decision-making; Cloud boundary network security; Intelligent agent reinforcement learning; Offensive and defensive game; Active defense decision-making; Cloud boundary network security; Offensive and defensive game; Intelligent agent reinforcement learning
• ISSN: 2667-2952; 2667-2952
• DOI: 10.1016/j.hcc.2023.100145

The cloud boundary network environment is characterized by a passive defense strategy, discrete defense actions, and delayed defense feedback in the face of network attacks, ignoring the influence of the external environment on defense decisions, thus resulting in poor defense effectiveness. Therefore, this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent, designs the network structure of the intelligent agent attack and defense game, and depicts the attack and defense game process of cloud boundary network; constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment, and portrays the interaction process between intelligent agent and environment; establishes the reward mechanism based on the attack and defense gain, and encourage intelligent agents to learn more effective defense strategies. the designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics, interaction lag, and control dispersion in the defense decision process of cloud boundary networks, and improve the autonomy and continuity of defense decisions.