检索结果-内蒙古大学图书馆

Sliding mode control strategy based on intermediate variable observer for cyber-physical systems under false data injection attack

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2025年

作者： Chen, Yunjun Liang, Zhanchuo Zhang, Lu Tiangong Univ Sch Control Sci & Engn 399 Binshuixidao Tianjin 300387 Peoples R China Tiangong Univ Tianjin Key Lab Intelligent Control Elect Equipmen Tianjin Peoples R China

A sliding mode control problem based on intermediate variable observer for non-linear cyber-physical systems in the existence of false data injection attack on actuator is investigated in this article. By constructing an intermediate variable observer to obtain accurate system states and reconstruct attack signals, based on which a non-singular terminal sliding mode control strategy is proposed to eliminate the effects of false data injection attack on the system. This strategy increases the convergence speed and transient response of the system in comparison with existing results. Furthermore, a continuous switching term is designed to suppress controller chattering. Parameters of the intermediate variable observer are determined by solving linear matrix inequality. The proposed method is applied to an electric amplidyne system for verifying its superiority.

关键词： Cyber-physical systems false data injection attack intermediate variable observer sliding mode control

来源：评论

学校读者我要写书评

暂无评论

Submodularity-Based false data injection attack Strategy in DC Microgrids

引用

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 2342-2356页

作者： Liu, Qi Zhao, Chengcheng Liu, Mengxiang Deng, Ruilong Cheng, Peng Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Univ Sheffield Dept Automat Control & Syst Engn Sheffield S10 2TN England

Despite significantly enhancing system flexibility and reliability, the adoption of distributed secondary control in DC microgrids (DCmGs) introduces new vulnerabilities to false data injection (FDI) attacks. As a typical FDI attack, the zero trace stealthy (ZTS) attack has been recently disclosed for DCmGs, which can deteriorate the control objective while keeping stealthy to unknown input observer (UIO)-based detectors. In this work, we investigate the optimal deployment of ZTS attacks, where the adversary with limited resources aims to compromise a set of communication links such that the system state convergence error can be maximized. Specifically, we formulate the optimal ZTS attack deployment problem as a combinatorial optimization problem and unveil its NP-hard characteristic. Then, we discover the submodularity in the state convergence error function, enabling us to transform the original NP-hard problem into a tractable submodular maximization problem. Furthermore, based on the submodular optimization theory, we propose a novel distributed algorithm for the optimal ZTS attack deployment in DCmGs, which effectively balances the attack benefits and computation cost. Finally, comparisons between the centralized and distributed algorithms are illustrated through extensive simulations.

关键词： Vectors Convergence Voltage control Microgrids Observers Costs Symbols Protocols Optimization Load modeling DC microgrid false data injection attack submodularity resource limitation

来源：评论

学校读者我要写书评

暂无评论

Enhancing cybersecurity via attribute reduction with deep learning model for false data injection attack recognition

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-31页

作者： Alrslani, Faheed A. F. Alohali, Manal Abdullah Aljebreen, Mohammed Alqahtani, Hamed Alshuhail, Asma Alshammeri, Menwa Almukadi, Wafa Sulaiman Northern Border Univ Fac Comp & Informat Technol Dept Informat Technol Rafha Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Saud Univ Community Coll Dept Comp Sci POB 28095 Riyadh 11437 Saudi Arabia King Khalid Univ Coll Comp Sci Ctr Artificial Intelligence Dept Informat SystUnit Cybersecur Abha Saudi Arabia King Faisal Univ Coll Comp Sci & Informat Technol Dept Informat Syst Al Hufuf Saudi Arabia Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakaka Saudi Arabia Univ Jeddah Coll Engn & Comp Sci Dept Software Engn Jeddah Saudi Arabia

Cyberattacks have given rise to several phenomena and have raised concerns among users and power system operators. When they are built to bypass state estimation bad data recognition methods executed in the conventional grid system control room, false data injection attacks (FDIA) pose a significant security threat to the operation of power systems. Therefore, real-time monitoring becomes inevitable with the quick implementation of renewables within the grid operator. The state estimation algorithm plays a major role in defining the grid's operating scenarios. FDIA creates a significant risk to these estimation strategies by adding malicious information to the measurement obtained. Real-time recognition of these attack classes improves grid resiliency and ensures a secure grid operation. This study introduces a novel Attribute Reduction with a Deep Learning-based false data injection attack Recognition (ARDL-FDIAR) technique. The primary goal of the ARDL-FDIAR technique is to improve security via the FDIA detection process. The ARDL-FDIAR technique uses Z-score normalization to scale the input data. The attribute reduction process gets invoked using the modified Lemrus optimization algorithm (MLOA) to choose optimal feature sets. Moreover, the FDIA detection process is performed by modelling an improved deep belief network (IDBN) model. Furthermore, the performance of the IDBN model is improved by the Cetacean Optimization Algorithm (COA)-based hyperparameter tuning process. A series of experiments were performed to ensure the enhancement of the ARDL-FDIAR technique. The results indicated the enhanced security performance of the ARDL-FDIAR technique compared to other DL approaches.

关键词： false data injection attack Cyberattack Deep learning Cetacean optimization Algorithm Deep Belief Network

来源：评论

学校读者我要写书评

暂无评论

false data injection attack detection for nuclear reactor based on chaotic time/frequency-hopping spread spectrum

引用

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2023年 176卷 841-851页

作者： Huang, Yu Wang, Jiajun Gu, He Zhou, Xinpeng Xie, Jiale Wang, Dongfeng North China Elect Power Univ Dept Automat Baoding 071003 Hebei Peoples R China North China Elect Power Univ Dept Automat Yonghua Bei Rd 619 Baoding 071003 Hebei Peoples R China

false data injection (FDI) attacks represent a serious threat to securing nuclear reactor operation. Efficient FDI attack detection is essential to prevent related unforeseen nuclear accidents. However, owing to their design principles, the existing detection methods are limited in their ability to detect multiple types of FDI attacks. To address this issue, we propose a detection scheme for FDI attacks on a nuclear reactor based on the chaotic time/ frequency-hopping (TH/FH) spread spectrum. The proposed scheme uses frequency hopping modulation, demodulation, and filtering of the signal based on hyper-chaotic sequences to limit the attacker's access to the system's real data and eliminate the adverse effects of attacks on the system. Furthermore, theoretical analysis derives the stability constraint of the frequency hopping frequency and demonstrates the detectability and defendability of the proposed scheme. Experimental simulations demonstrated that our proposed scheme can detect two typical FDI attacks, replay attacks and covert attacks, without compromising system operation, and defend against and reproduce covert attack signals.

关键词： Nuclear reactor attack detection false data injection attack Chaotic time frequency-hopping spread spectrum

来源：评论

学校读者我要写书评

暂无评论

false data injection attack on consensus-based distributed estimation

引用

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2017年第9期27卷 1419-1432页

作者： Lei, L. Yang, W. Yang, C. Shi, H. B. East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

We consider security issues for consensus-based distributed estimation problem. In a sensor network, an attacker with limited power injects random false data into the communication links so as to degrade the network performance. First, an optimal estimator is designed by minimizing the mean-squared estimation error of each sensor under hostile attacks. Then, a sufficient condition is provided to guarantee the stability of the proposed estimator. Finally, a set of suboptimal attacking sequences is obtained for the attacker to maximize the network estimation error. Illustrative examples are provided to verify the effectiveness of the suboptimal attacking strategy. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： consensus-based distributed estimation false data injection attack limited power optimal attacking sequences

来源：评论

学校读者我要写书评

暂无评论

false data injection attack in a Platoon of CACC: Real-Time Detection and Isolation With a PDE Approach

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第7期23卷 8692-8703页

作者： Biroon, Roghieh A. Biron, Zoleikha Abdollahi Pisu, Pierluigi Clemson Univ Dept Elect & Comp Engn Clemson SC 29607 USA Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA Clemson Univ Dept Automot Engn Clemson SC 29607 USA

Connected vehicles are potential solutions to address some of the existing challenges in transportation systems, such as emission, traffic congestion, and fuel consumption. However, vehicular communication networks endure from reliability and security issues. Cyber-attacks with performance-disrupting purposes can lead to catastrophic collisions in connected vehicles and increase traffic congestion. To ensure safety and reliability, a monitoring system with the capability of diagnosing cyber-attacks is necessary for connected vehicles. In this study, we consider a platoon of connected vehicles equipped with cooperative adaptive cruise control (CACC) that is subjected to a specific type of cyber-attack, namely "false. data injection" (FDI) attack. A smart FDI attack is modeled with ghost vehicles injection into the connected vehicles network to disrupt the performance of the whole system. To ease the analysis, we develop a partial differential equation (PDE) model for the CACC vehicle dynamics using its ordinary differential equations (ODE). Furthermore, a PDE observer is designed to detect the FDI attack and locate the attack's injection point in the platoon. Lyapunov stability theory has been utilized to verify the observer's convergence under no-attack scenario and study residuals' behaviors in the presence of the attack. A non-zero constant threshold is considered to provide robustness in attack detection despite the measurement noises. Eventually, the effectiveness of the proposed algorithm is evaluated with simulation studies.

关键词： Connected vehicles detection estimation false data injection attack ghost vehicle observer design partial differential equation

来源：评论

学校读者我要写书评

暂无评论

false data injection attack for Cyber-Physical Systems With Resource Constraint

引用

IEEE TRANSACTIONS ON CYBERNETICS 2020年第2期50卷 729-738页

作者： Li, Fangfei Tang, Yang East China Univ Sci & Technol Dept Math Shanghai 200237 Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

Cyber-security is of the fundamental importance for cyber-physical systems (CPSs), since CPSs are vulnerable to cyber attack. In order to make the defensive measures better, one needs to understand the behavior from the view of an attacker. In this paper, the problem of false data injection attack on remote state estimation with resource constraints is studied in two cases, where the first case is that the attacker adds a Gaussian noise to the innovation, while the other is that the attacker employs a Gaussian noise to replace the innovation. In addition, the attacker is assumed to has a resource constraint, i.e., he/she cannot attack all the sensors, at the same time should decide which sensors to attack. By using the matrix theory, the optimal attack strategy problem, which aims to maximize the trace of the remote estimation error covariance, is converted into a convex optimization problem that can be solved. Thus, an optimal attack strategy is given to illustrate which sensors should be attacked. An example is given to show the effectiveness of the theoretical results.

关键词： Sensors Cyberattack Multidimensional systems Covariance matrices Optimization State estimation Kalman filters Convex optimization cyber-physical systems (CPSs) false data injection attack remote estimator estimation

来源：评论

学校读者我要写书评

暂无评论

false data injection attack Detection Method Based on Deep Learning With Multi-Scale Feature Fusion

引用

IEEE ACCESS 2024年 12卷 89262-89274页

作者： Ji, Jinpeng Liu, Yang Chen, Jian Yao, Zhiwei Zhang, Mengdi Gong, Yanyong Shandong Univ Technol Sch Elect & Elect Engn Zibo 255000 Peoples R China Zibo Metrol Technol Res Inst Zibo 255025 Peoples R China

Cyber-attacks, especially the false data injection attack (FDIA), are gradually becoming a common way to threaten the regular operation of power grid. However, the FDIA is challenging to detect because it prevents the bad data detection mechanism in the energy management system from destroying the integrity of measurement information. Aiming at the problem of the FDIA detection in smart grids, this paper presents a FDIA detection method based on deep learning with multi-scale feature fusion. First, the improved convolution neural network (ICNN) is used to predict measurement data by combining convolution neural network with the Inception v1 module. Then, the attention mechanism is introduced into the ICNN to extract and fuse full and partial features of measurement data. By fitting the function between measurement and state vectors, the state data are generated with predicted measurement data. Eventually, the threshold of divergence is obtained to determine whether the FDIA occurs or not by the difference in probability distribution between predicted and actual state vectors. The performance of the proposed method is evaluated in the IEEE 14-node and 39-node test systems. The results show that the proposed method can accurately detect the existence of FDIA in time. This method has definite robustness to noise and distributed generation switching.

关键词： Feature extraction Vectors Logic gates data models Training Topology Convolutional neural networks Cyberattack false data injection attack convolution neural network feature fusion multi-scale convolution dynamic state estimation

来源：评论

学校读者我要写书评

暂无评论

false data injection attack for Switched Systems

引用

IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 1754-1759页

作者： Zhao, Rui Zuo, Zhiqiang Wang, Yijing Zhang, Wentao Tianjin Univ Sch Elect & Informat Engn Tianjin Key Lab Intelligent Unmanned Swarm Technol Tianjin Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

The letter studies the secure state estimation problem for switched systems. The single/joint false data injection attacks are designed with the aim at altering the sensor signal and/or switching signal. Firstly, it is shown that the attack will steer system state to infinity but could be detectable by ?2 detector when only the switching signal is attacked. In addition, the attack acting on sensor signal is designed, which can be recognized by the summation (SUM) detector but fails by ?2 detector. Then a joint attack strategy is devised and a sufficient condition is given to guarantee that the joint attack is strictly stealthy. The joint attack performs well since it can launch a strictly stealthy attack compared with the sensor signal attack. Finally, a numerical example is given to verify the theoretical results.

关键词： Detectors Switches Switched systems Control systems Estimation System performance System dynamics false data injection attack switched system joint attack stealthy attack

来源：评论

学校读者我要写书评

暂无评论

false data injection attack on Distributed State Estimation over a Wireless Sensor Network 35

False Data Injection Attack on Distributed State Estimation ...

引用

第35届中国控制会议

作者： LEI Li YANG Wen YANG Chao SHI Hongbo YAN Huaicheng School of information Science and Engineering East China University of Science and Technology

ISBN: (纸本)9781509009107

Security risks of wireless sensor networks have attracted increasing concerns in recent *** this paper,we consider an attacker with limited power who injects random false data into the transmitted data on the communication links so as to maximize the network estimation ***,an optimal estimator is obtained by minimizing the mean-squared estimation error of each sensor under ***,a sufficient condition is provided to guarantee the stability of the proposed ***,a set of optimal attacking sequences are obtained to maximize the network estimation *** examples are provided to verify the effectiveness of the optimal attacking sequences.

关键词： distributed state estimation false data injection attack limited power optimal attack sequences

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：