Design and Analysis of Decentralized Interactive Cyber Defense Approach based on Multi-agent Coordination

Liu, Ming; Ma, Lu; Li, Chao; Chang, Wen Hsin; Wang, Yuanjie; Cui, Jianming; Yingying, Ji

doi:10.1109/msn50589.2020.00110

2020 16th International Conference on Mobility, Sensing and Networking (MSN) 2020

DOI: 10.1109/msn50589.2020.00110

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Design and Analysis of Decentralized Interactive Cyber Defense Approach based on Multi-agent Coordination

Ming Liu

Lu Ma

Chao Li

et al.

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2021

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An Interactive Self-Learning Game and Evolutionary Approach Based on Non-Cooperative Equilibrium

Zhao

Zhang

et al. 2021

Electronics

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Most current studies on multi-agent evolution based on deep learning take a cooperative equilibrium strategy, while interactive self-learning is not always considered. An interactive self-learning game and evolution method based on non-cooperative equilibrium (ISGE-NCE) is proposed to take the benefits of both game theory and interactive learning for multi-agent confrontation evolution. A generative adversarial network (GAN) is designed combining with multi-agent interactive self-learning, and the non-cooperative equilibrium strategy is well adopted within the framework of interactive self-learning, aiming for high evolution efficiency and interest. For assessment, three typical multi-agent confrontation experiments are designed and conducted. The results show that, first, in terms of training speed, the ISGE-NCE produces a training convergence rate of at least 46.3% higher than that of the method without considering interactive self-learning. Second, the evolution rate of the interference and detection agents reaches 60% and 80%, respectively, after training by using our method. In the three different experiment scenarios, compared with the DDPG, our ISGE-NCE method improves the multi-agent evolution effectiveness by 43.4%, 50%, and 20%, respectively, with low training costs. The performances demonstrate the significant superiority of our ISGE-NCE method in swarm intelligence.

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An Interactive Self-Learning Game and Evolutionary Approach Based on Non-Cooperative Equilibrium

Zhao

Zhang

et al. 2021

Electronics

View full text Add to dashboard Cite

show abstract

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Design and Analysis of Decentralized Interactive Cyber Defense Approach based on Multi-agent Coordination

Cited by 1 publication

References 22 publications

An Interactive Self-Learning Game and Evolutionary Approach Based on Non-Cooperative Equilibrium

An Interactive Self-Learning Game and Evolutionary Approach Based on Non-Cooperative Equilibrium

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