摘要翻译:
传感器如相量测量单元(PMUs)被赋予GPS接收器普遍安装,提供实时的网格可见性。多个PMU可以协作地启用状态估计例程。然而,GPS欺骗攻击可以显著地改变PMU测量,误导网络运营商,并严重影响后续的纠正控制行动。利用一个新的测量模型,明确地解释了GPS欺骗攻击,提出了一个优化问题,以识别网络中最易受攻击的PMU。提出了一种贪婪算法来解决上述问题。此外,本文还提出了一种计算效率较高的交替最小化算法,用于联合状态估计和攻击重构。在IEEE基准网络上的数值试验验证了所提出的方法。
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英文标题:
《Vulnerability Analysis of Smart Grids to GPS Spoofing》
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作者:
Paresh Risbud, Nikolaos Gatsis, and Ahmad Taha
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最新提交年份:
2018
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Systems and Control 系统与控制
分类描述:cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
cs.sy是eess.sy的别名。本部分包括理论和实验研究,涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制,以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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一级分类:Computer Science 计算机科学
二级分类:Cryptography and Security 密码学与安全
分类描述:Covers all areas of cryptography and security including authentication, public key cryptosytems, proof-carrying code, etc. Roughly includes material in ACM Subject Classes D.4.6 and E.3.
涵盖密码学和安全的所有领域,包括认证、公钥密码系统、携带证明的代码等。大致包括ACM主题课程D.4.6和E.3中的材料。
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一级分类:Electrical Engineering and Systems Science 电气工程与系统科学
二级分类:Signal Processing 信号处理
分类描述:Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用,包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、(生物)医学、图像、视频和多模态自然和人为信号,包括通信信号和数据。感兴趣的主题包括:统计信号处理、谱估计和系统辨识;滤波器设计;自适应滤波/随机学习;(压缩)采样、传感和变换域方法,包括快速算法;用于机器学习的信号处理和用于信号处理应用的机器学习;网络与图形信号处理;信号处理中的凸和非凸优化方法;雷达、声纳和传感器阵列波束形成和测向;通信信号处理;低功耗、多核、片上系统信号处理;信息物理系统的传感、通信、分析和优化,如电网和物联网。
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英文摘要:
Sensors such as phasor measurement units (PMUs) endowed with GPS receivers are ubiquitously installed providing real-time grid visibility. A number of PMUs can cooperatively enable state estimation routines. However, GPS spoofing attacks can notably alter the PMU measurements, mislead the network operator, and drastically impact subsequent corrective control actions. Leveraging a novel measurement model that explicitly accounts for the GPS spoofing attacks, this paper formulates an optimization problem to identify the most vulnerable PMUs in the network. A greedy algorithm is developed to solve the aforementioned problem. Furthermore, the paper develops a computationally efficient alternating minimization algorithm for joint state estimation and attack reconstruction. Numerical tests on IEEE benchmark networks validate the developed methods.
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PDF链接:
https://arxiv.org/pdf/1804.0931


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