基于深度强化学习的网络物理系统DoS攻击能量优化分配

IF 8.9 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Internet of Things Journal Pub Date : 2025-07-10 DOI:10.1109/jiot.2025.3587647

Junyong Jiang, Buwei Tian, Xin Yuan, Yubin Jia, Lu Dong

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Optimal DoS Attack Energy Allocation in Cyber-Physical Systems Based on Deep Reinforcement Learning

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来源期刊

IEEE Internet of Things Journal Computer Science-Information Systems

CiteScore

17.60

自引率

13.20%

发文量

1982

期刊介绍： The EEE Internet of Things (IoT) Journal publishes articles and review articles covering various aspects of IoT, including IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Topics encompass IoT's impacts on sensor technologies, big data management, and future internet design for applications like smart cities and smart homes. Fields of interest include IoT architecture such as things-centric, data-centric, service-oriented IoT architecture; IoT enabling technologies and systematic integration such as sensor technologies, big sensor data management, and future Internet design for IoT; IoT services, applications, and test-beds such as IoT service middleware, IoT application programming interface (API), IoT application design, and IoT trials/experiments; IoT standardization activities and technology development in different standard development organizations (SDO) such as IEEE, IETF, ITU, 3GPP, ETSI, etc.