检索结果-内蒙古大学图书馆

Distributed Secure State Estimation for Cyber-Physical Systems Under false data injection attacks

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第5期11卷 4443-4455页

作者： Zhang, Xin-Yu Yang, Guang-Hong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

This paper studies the distributed secure state estimation problem for cyber-physical systems subjected to false data injection attacks. A novel distributed framework is proposed for remote state estimation based on distributed Kalman filtering technology, where the sensors gather neighboring measurement information and send it and local estimation to remote estimators through wireless channels. At the remote end, each estimator is equipped with a threshold-based protector. Compared with the existing results, the proposed protectors are advantageous in terms of saving calculation time. The optimal Kalman gain and the convergence of error covariance are provided under two different attack modes. Finally, a numerical simulation is conducted to verify the effectiveness of state estimation.

关键词： Cyber-physical systems distributed Kalman filtering false data injection attacks security Cyber-physical systems distributed Kalman filtering false data injection attacks security

来源：评论

学校读者我要写书评

暂无评论

DAMGAT-Based Interpretable Detection of false data injection attacks in Smart Grids

引用

IEEE TRANSACTIONS ON SMART GRID 2024年第4期15卷 4182-4195页

作者： Su, Xiangjing Deng, Chao Yang, Jiajia Li, Fengyong Li, Chaojie Fu, Yang Dong, Zhao Yang Shanghai Univ Elect Power Engn Res Ctr Offshore Wind Technol Minist Educ Shanghai 200090 Peoples R China Shanghai Univ Elect Power Offshore Wind Power Res Inst Shanghai 200090 Peoples R China Shanghai Univ Elect Power Coll Elect Engn Shanghai 200090 Peoples R China James Cook Univ Coll Sci & Engn Townsville Qld 4811 Australia Shanghai Univ Elect Power Coll Comp Sci & Technol Shanghai 200090 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

false data injection attacks (FDIAs) significantly disrupt the secure operation of smart grids by manipulating the measured values collected by intelligent instruments. Existing studies have utilized deep learning techniques to enhance the detection of FDIAs, however, these studies often overlook the spatial correlation between power grid topology and measurement data. Meanwhile, the high complexity of deep neural network severely impedes the interpretability of detection models, resulting in the incredibility of detection results. To address the above challenges, this paper proposes an interpretable deep learning FDIAs detection method, named dual-attention multi-head graph attention network, DAMGAT. The DAMGAT introduces a dual-attention mechanism that incorporates both node feature attention and spatial topology attention into a multi-head graph attention network. This mechanism efficiently aggregates attack characteristics and spatial topology information by dynamically capturing the potential correlations between FDIAs detection and measurement data. Furthermore, the proposed model can provide clear and credible interpretations for high-accuracy detection results via analyzing features and spatial topology attention weights. Extensive simulations are performed using the IEEE 14-bus and 118-bus test systems. The experimental results demonstrate that the proposed model outperforms state-of-the-art FDIA detection methods in terms of accuracy, while also providing reasonable interpretability for features and spatial dimensions.

关键词： Smart grids Deep learning Feature extraction Adaptation models Topology Power measurement data models false data injection attacks smart grid interpretable deep learning graph neural network attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Observer-based dynamic event-triggered secure control for nonlinear networked control systems with false data injection attacks

引用

INFORMATION SCIENCES 2023年 644卷

作者： Yang, Meng Zhai, Junyong Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China

This paper considers the problem of observer-based dynamic event-triggered secure control for nonlinear networked control systems (NCSs) under false data injection attacks (FDIA). A novel output feedback controller is developed by utilizing a sampled-data observer to relieve the impacts of unmeasured states and FDIA. To make full use of the limited network resources, a dynamic periodic event-triggered mechanism (PETM) with an auxiliary variable is designed, which can reduce the transmission of information. Then, an approximation of injected false data is implemented based on an adaptive neural network (NN) in the presence of FDIA. Based on the proposed control strategy, the boundness of the nonlinear NCSs can be ensured by selecting some proper parameters. Finally, the effectiveness and validity of the proposed control strategy are illustrated via simulation of a single link robot arm system.

关键词： false data injection attacks Nonlinear networked control systems Dynamic event-triggered control Neural networks Observer

来源：评论

学校读者我要写书评

暂无评论

Fast Screening of High-Risk Lines Under false data injection attacks

引用

IEEE TRANSACTIONS ON SMART GRID 2019年第4期10卷 4003-4014页

作者： Che, Liang Liu, Xuan Li, Zuyi Hunan Univ Dept Elect & Informat Engn Changsha 410000 Hunan Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China IIT Elect & Comp Engn Chicago IL 60616 USA

false data injection attacks can compromise power system operational security by causing severe branch overloads. A set of transmission lines can be more vulnerable to the attack. These lines can be quickly identified by a high-risk line fast screening (HLFS) approach proposed in this paper. The HLFS has a high computational efficiency which facilitates its applications in large-scale systems. In the meantime, for an in-depth security assessment, a critical-line identification tool is also presented to identify a subset of critical lines with simultaneous tripping risk under the attack. The effectiveness and efficiency of the proposed approach are verified by benchmarking its performance against a basic linear-programming checking method in the case study which includes simulations on multiple large-scale MATPOWER cases.

关键词： Cyber-security false data injection attacks overload risk line screening vulnerability assessment

来源：评论

学校读者我要写书评

暂无评论

Leader-following event-triggered consensus control of MASs under false data injection attacks

引用

SYSTEMS & CONTROL LETTERS 2023年 178卷

作者： Wang, Meirong Hu, Jianqiang Xu, Wenying Cao, Jinde Southeast Univ Sch Math Nanjing 211189 Peoples R China Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea

This paper studies the event-triggered consensus problem of leader-following linear multi-agent systems (MASs) under stochastic false data injection attacks (FDIAs), where the false data is injected into the communication channels between the network agents. The randomly occurring stealthy FDIAs on the communication channels are modeled by the Bernoulli process distributions. By designing a distributed event-triggered consensus control strategy, the consensus problem of the MAS with one-to-all FDIA scenario is studied firstly, then it is extended to the more flexible one-to-one FDIA scenario with a maximum number of destroyed neighboring channels for each agent. The event triggered mechanism of the state-dependent threshold is proposed and the auxiliary variable is introduced to further prevent Zeno behavior. By utilizing stochastic analysis method, two bounded consensus theorems for directed MAS networks are presented for the above two cases of attack signals respectively to guarantee the followers synchronize to the leader within a limited margin of error ultimately. Finally, simulation results are provided to illustrate the validity and feasibility of the proposed event-triggered consensus control strategies.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Multi-agent system false data injection attacks Stochastic attacks Event-triggered control Bounded consensus

来源：评论

学校读者我要写书评

暂无评论

Sampled-data consensus control for nonlinear time-delay multi-agent systems under false data injection attacks

引用

NONLINEAR DYNAMICS 2023年第13期111卷 12253-12266页

作者： Duan, Zhiyu Wei, Airong Zhang, Xianfu Mu, Rui Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China

In this paper, the consensus problem is investigated via sampled-data output feedback control for nonlinear multi-agent systems subject to output delay and false data injection attacks, where only the delayed sampling outputs are available. Firstly, employing the available information, an improved compensator that does not continuously communicate with neighbors is constructed to provide control signals. Secondly, in the absence and presence of false data injection attacks, two compensator-based sampled-data control protocols with different time-varying gains are put forward, respectively. Then, in combination with the Lyapunov-Krasovskii functional approach, the proposed protocols are capable of achieving consensus and compensating for the effect of output delay and attack signals. Furthermore, above results are extended to the multi-agent systems with more general nonlinearities. Particularly, the sampling period and the output delay are arbitrary positive constants, removing the constraints of upper bound. Finally, the feasibility of the proposed protocols is demonstrated by two practical simulations.

关键词： Nonlinear multi-agent systems Output delay false data injection attacks Sampled-data control Time-varying gain

来源：评论

学校读者我要写书评

暂无评论

Survey of machine learning methods for detecting false data injection attacks in power systems

引用

IET SMART GRID 2020年第5期3卷 581-595页

作者： Sayghe, Ali Hu, Yaodan Zografopoulos, Ioannis Liu, XiaoRui Dutta, Raj Gautam Jin, Yier Konstantinou, Charalambos Florida State Univ Ctr Adv Power Syst FAMU FSU Coll Engn Tallahassee FL 32306 USA Univ Florida Dept Elect & Comp Engn Gainesville FL USA

Over the last decade, the number of cyber attacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, false data injection attacks (FDIAs) are a class of cyber-attacks against power grid monitoring systems. Adversaries can successfully perform FDIAs to manipulate the power system state estimation (SE) by compromising sensors or modifying system data. SE is an essential process performed by the energy management system towards estimating unknown state variables based on system redundant measurements and network topology. SE routines include bad data detection algorithms to eliminate errors from the acquired measurements, e.g. in case of sensor failures. FDIAs can bypass BDD modules to inject malicious data vectors into a subset of measurements without being detected, and thus manipulate the results of the SE process. To overcome the limitations of traditional residual-based BDD approaches, data-driven solutions based on machine learning algorithms have been widely adopted for detecting malicious manipulation of sensor data due to their fast execution times and accurate results. This study provides a comprehensive review of the most up-to-date machine learning methods for detecting FDIAs against power system SE algorithms.

关键词： security of data power grids power system security power engineering computing power system measurement energy management systems power system state estimation binary decision diagrams learning (artificial intelligence) power system state estimation system data energy management system unknown state variables system redundant measurements data detection algorithms FDIA malicious data vectors data-driven solutions machine learning algorithms sensor data power system SE algorithms false data injection attacks power systems cyber attacks cyber-attacks power grid monitoring systems

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective false data injection attacks of Cyber-Physical Power Systems

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第9期69卷 3924-3928页

作者： Lu, Kang-Di Wu, Zheng-Guang Zhejiang Univ Inst Cyber Syst & Control Hangzhou 310027 Peoples R China Chengdu Univ Inst Adv Study Chengdu 610106 Peoples R China Zhejiang Normal Univ Coll Math & Comp Sci Jinhua 321004 Zhejiang Peoples R China

In cyber-physical power systems (CPPSs), false data injection attack (FDIA) has drawn much attention due to its stealthiness. It is of great importance to investigate the potential behaviors of attackers to improve the cyber-security of CPPSs. However, most FDIA models are often constructed separately on the effect of attackers or the impact of attacks. Accordingly, this brief proposes a multi-objective stealthy FDIA scheme in AC grid model. The attack model is described as a multi-objective optimization problem, where minimization of contaminated measurements and maximization of the attack impact are considered as two objectives while remaining stealthy. To deal with the established attack model, a non-dominated sorting genetic algorithm II (NSGAII) is introduced as the solver. To improve the efficiency of generating attack vector, a new representation mechanism is proposed to describe locations and values of injected states. Additionally, during the evolutionary process, we propose a mutation operation to balance the sparsity and impact of FDIA. Simulation results on the IEEE 14-bus, 30-bus, and 118-bus systems demonstrate the feasibility of the NSGAII-based FDIA model.

关键词： Biological system modeling Pollution measurement Computational modeling Sociology Optimization Mathematical models Sorting Cyber-physical power systems cyber-security false data injection attacks multi-objective optimization problem genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

The 2015 Ukraine Blackout: Implications for false data injection attacks

引用

IEEE TRANSACTIONS ON POWER SYSTEMS 2017年第4期32卷 3317-3318页

作者： Liang, Gaoqi Weller, Steven R. Zhao, Junhua Luo, Fengji Dong, Zhao Yang Univ Newcastle Ctr Intelligent Elect Networks Callaghan NSW 2308 Australia Univ Newcastle Sch Elect Engn & Comp Sci Callaghan NSW 2308 Australia Chinese Univ Hong Kong Shenzhen Shenzhen 518172 Guangdong Peoples R China Univ Sydney Sch Civil Engn Sydney NSW 2006 Australia Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

In a false data injection attack (FDIA), an adversary stealthily compromises measurements from electricity grid sensors in a coordinated fashion, with a view to evading detection by the power system bad data detection module. A successful FDIA can cause the system operator to perform control actions that compromise either the physical or economic operation of the power system. In this letter, we consider some implications for FDIAs arising from the late 2015 Ukraine Blackout event.

关键词： Cyber-attacks false data injection attacks ukraine blackout

来源：评论

学校读者我要写书评

暂无评论

false data injection attacks detection with modified temporal multi-graph convolutional network in smart grids

引用

COMPUTERS & SECURITY 2023年 124卷

作者： Han, Yinghua Feng, Hantong Li, Keke Zhao, Qiang Northeastern Univ Qinhuangdao Sch Comp & Commun Engn Qinhuangdao Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao Peoples R China

false data injection Attack (FDIA) detection can prevent the tampering of important data in the smart grid. This is of great significance to the operation and control of modern power systems. Since the exist-ing FDIA detection methods are limited by the sequential input of data in Euclidean space, they cannot accurately describe the compelling correlation between data components. Therefore, this paper proposes a novel FDIA localization detection method based on graph data modeling and graph deep learning. The proposed approach tries to disaggregate the primary data into graph structured data with graph topo-logical relationships based on graph theory, then designs specialized networks for data with different graph topologies. Moreover, the designed multi-graph mechanism and temporal correlation layer can bet-ter fully mine the correlation features between data components, with its attribute characteristics, to construct deep learning on the specific graph topology for FDIA detection. Extensive simulation experi-ments and visualization show that the proposed scheme is more effective than the conventional detection model, and its overall accuracy in 14-bus, 118-bus and 30 0-bus systems is 98.3%, 96.4% and 95.8%. It also proves that this scheme has high robustness and generalization ability in different scenarios.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Power system Graph convolutional network Gated recursive unit false data injection attacks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：