利用垂直联合学习的创新多代理方法,实现稳健的网络物理系统

IF 4.4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shivani Gaba , Ishan Budhiraja , Vimal Kumar , Sahil Garg , Mohammad Mehedi Hassan
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引用次数: 0

摘要

联合学习是在分散环境中训练人工智能系统的一种引人注目的方法,与传统的集中式训练方法相比,它优先考虑数据安全。要在持续数据交换框架内开发出能抵御各种攻击的网络物理系统,了解数据流中表现出异常行为的高层威胁之间的关联性至关重要。这项工作引入了一个新颖的垂直联合多代理学习框架,以应对在静态和非静态垂直联合学习环境中对攻击者和防御者代理建模的挑战。在静态环境中,我们的方法独特地应用了基于同步深度 Q 网络(DQN)的代理,促进了向最优策略的收敛。相反,在非静态环境中,我们采用基于同步优势行动者批判者(A2C)的代理,以适应多代理垂直联合强化学习(VFRL)环境的动态特性。这种方法使我们能够模拟和分析攻击方和防御方代理之间的对抗性相互作用,确保政策制定的稳健性。我们的详尽分析证明了我们方法的有效性,展示了它在静态和动态设置中学习最优策略的能力,从而极大地推动了联合学习环境下的网络安全领域。为了评估我们方法的有效性,我们对其与基准方案进行了比较分析。我们的研究结果表明,与标准方法相比,我们的方法有了显著提高,证实了我们方法的有效性。这一进展通过促进实质性政策的制定,极大地增强了联合学习背景下的网络安全领域。与 A3C、DDQN、DQN 和 Reinforce 相比,拟议方案分别提高了 15.93%、32.91%、31.02% 和 47.26%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An innovative multi-agent approach for robust cyber–physical systems using vertical federated learning

Federated learning presents a compelling approach to training artificial intelligence systems in decentralized settings, prioritizing data safety over traditional centralized training methods. Understanding correlations among higher-level threats exhibiting abnormal behavior in the data stream becomes paramount to developing cyber–physical systems resilient to diverse attacks within a continuous data exchange framework. This work introduces a novel vertical federated multi-agent learning framework to address the challenges of modeling attacker and defender agents in stationary and non-stationary vertical federated learning environments. Our approach uniquely applies synchronous Deep Q-Network (DQN) based agents in stationary environments, facilitating convergence towards optimal strategies. Conversely, in non-stationary contexts, we employ synchronous Advantage Actor–Critic (A2C) based agents, adapting to the dynamic nature of multi-agent vertical federated reinforcement learning (VFRL) environments. This methodology enables us to simulate and analyze the adversarial interplay between attacker and defender agents, ensuring robust policy development. Our exhaustive analysis demonstrates the effectiveness of our approach, showcasing its capability to learn optimal policies in both static and dynamic setups, thus significantly advancing the field of cyber-security in federated learning contexts. To evaluate the effectiveness of our approach, we have done a comparative analysis with its baseline schemes. The findings of our study show significant enhancements compared to the standard methods, confirming the efficacy of our methodology. This progress dramatically enhances the area of cyber-security in the context of federated learning by facilitating the formulation of substantial policies. The proposed scheme attains 15.93%, 32.91%, 31.02%, and 47.26% higher results as compared to the A3C, DDQN, DQN, and Reinforce, respectively.

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来源期刊
Ad Hoc Networks
Ad Hoc Networks 工程技术-电信学
CiteScore
10.20
自引率
4.20%
发文量
131
审稿时长
4.8 months
期刊介绍: The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to: Mobile and Wireless Ad Hoc Networks Sensor Networks Wireless Local and Personal Area Networks Home Networks Ad Hoc Networks of Autonomous Intelligent Systems Novel Architectures for Ad Hoc and Sensor Networks Self-organizing Network Architectures and Protocols Transport Layer Protocols Routing protocols (unicast, multicast, geocast, etc.) Media Access Control Techniques Error Control Schemes Power-Aware, Low-Power and Energy-Efficient Designs Synchronization and Scheduling Issues Mobility Management Mobility-Tolerant Communication Protocols Location Tracking and Location-based Services Resource and Information Management Security and Fault-Tolerance Issues Hardware and Software Platforms, Systems, and Testbeds Experimental and Prototype Results Quality-of-Service Issues Cross-Layer Interactions Scalability Issues Performance Analysis and Simulation of Protocols.
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