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Stealthy Attacks on Sampled-Data Control and Encryption-Based Countermeasures

IEEE Transactions on Automatic Control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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Fixed-time adaptive parameter estimation for magnetostrictive actuated positioning system

Fixed-time adaptive parameter estimation for magnetostrictiv...

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第43届中国控制会议

作者： Shengbin Wu Jing Na Yingbo Huang Faxiang Zhang Xinkai Chen Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Department of Electronic and Information Systems Shibaura Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

An adaptive parameter estimation method that ensures fixed-time convergence is proposed for micro-nano positioning system driven by magnetostrictive actuator(i.e., magnetostrictive actuated positioning system). Firstly, the hysteresis nonlinearity of the magnetostrictive actuated positioning system is characterized using the generalized Prandtl–Ishlinskii(GPI) ***, the low-pass filter is introduced to design the adaptive estimation law driven by the estimation error, by which the unmeasured intermediate hysteresis variable can be avoided, and the estimation error converges to zero within a fixed time. Specifically,a modified error variable is designed to deal with the influence of the regression vector, thereby improving convergence performance and simplifying parameter adjustment. Finally, the effectiveness of the presented method is verified by simulation and experimental outcomes.

关键词： Adaptive parameter estimation Fixed-time convergence Magnetostrictive actuator Micro/nano positioning system

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Hybrid polynomial-based trajectory planning for lower limb exoskeleton robots 18

Hybrid polynomial-based trajectory planning for lower limb e...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Li, Shupei Zhang, Chi Yi, Feng Lv, Pingping Yuan, Ting Zhang, Meitong University of Chinese Academy of Sciences Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315201 China Hebei University of Science & Technology School of Electrical Engineering Ningbo315201 China

ISBN: (纸本)9798350312201

To ensure smooth, continuous, and precise motion of lower limb exoskeleton robots, this paper proposes a 6-5-6 polynomial trajectory planning method (6-5-6 PTPM) that combines 5th and 6th polynomials. By imposing corresponding kinematic constraints, this method achieves the trajectory plan of seven sub-phases of the gait cycle on the kinematic model of the lower limb exoskeleton robot. The simulation is conducted and compared to the 4-3-4 polynomial trajectory planning method (4-3-4 PTPM). The comparison results indicate that the 6-5-6 PTPM is shown to have a lower error between the planned trajectory and the actual trajectory. Additionally, it avoids spikes of the acceleration curve that existed in the 4-3-4 polynomial method. The proposed method enables the acceleration curve smoother and mechanical impact lessened, which demonstrates the feasibility and stability of the proposed method. © 2023 IEEE.

关键词： Trajectories

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An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

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computers, Materials & Continua 2022年第4期71卷 199-213页

作者： Abdelhameed Ibrahim Hattan F.Abutarboush Ali Wagdy Mohamed Mohamad Fouad El-Sayed M.El-kenawy Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electrical Engineering Department College of EngineeringTaibah UniversityMedina42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyEgypt

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their *** size affects the quality factor and the radiation loss of the *** antennas can overcome the limitation of bandwidth for small *** learning(ML)model is recently applied to predict antenna *** can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated *** accuracy of the prediction depends mainly on the selected *** models combine two or more base models to produce a better-enhanced *** this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial *** base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the *** Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base *** proposed model is compared with three based models and the average ensemble *** results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

关键词： Metamaterial antenna machine learning ensemble model multilayer perceptron support vector machines

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements 63

A Multi-Player Potential Game Approach for Sensor Network Lo...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Waterloo Department of Mechanical and Mechatronics Engineering WaterlooONN2L 3G1 Canada Tongji University Department of Control Science and Engineering shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing China Aalborg University Department of Electronic Systems Denmark Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. © 2024 IEEE.

关键词： Nash equilibrium

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Text Analysis on Early Reactions to ChatGPT as a Tool for Academic Progress or Exploitation

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SN computer science 2024年第4期5卷 366页

作者： Bukar, Umar Ali Sayeed, Md Shohel Razak, Siti Fatimah Abdul Yogarayan, Sumendra Amodu, Oluwatosin Ahmed Raja Mahmood, Raja Azlina Centre for Intelligent Cloud Computing (CICC) Faculty of Information Science & amp Technology Multimedia University Jalan Ayer Keroh Lama Bukit Beruang Melaka 75450 Malaysia Department of Electrical Electronics & amp Systems Engineering Universiti Kebangsaan Malaysia (UKM) Selangor Bangi 43600 Malaysia Information and Communication Engineering Department Elizade University Ondo State Ilara-Mokin Nigeria Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Darul Ehsan Serdang 43400 Malaysia

Large language models (LLMs) have garnered significant attention lately, and one particular implementation that has captivated users is ChatGPT, a first-of-its kind innovation that sparks intense debates among professionals regarding its ethical boundaries, especially after it was released for public use. Some of the debates are ongoing, with several people getting more used to this tool and some losing touch with several ethical concerns. Thus, it is crucial to explore the early professional’s perspectives surrounding ChatGPT to comprehend its potential impact on the educational sector and, in particular, society in general. This research aims to address this knowledge gap by employing data mining techniques on LinkedIn to analyze ChatGPT-related discussions and provide a comprehensive textual examination. The dataset for this investigation consists of 166 records written in English by LinkedIn users discussing ChatGPT in its early stage. The study utilized VOSviewer software to clean, analyze, mine, and visualize the data. Among the sample, it was observed that 49% of the users are PhD holders, predominantly with experience in scientific research and data science fields. Hence, the findings reveal that issues such as plagiarism, references, citations, manuscripts, papers, and literature reviews stand out as the major concerns. These results suggest that while ChatGPT can contribute to academic progress by fostering new knowledge, thoughts, and ideas, it can also facilitate academic misconduct, including plagiarism and inaccurate information dissemination. Therefore, this paper also provides discussions on the implications and ethical issues of ChatGPT. In particular, it is imperative to establish clear ethical boundaries and guidelines for the appropriate use of LLMs in academic settings. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Academic ChatGPT Ethics LinkedIn LLMs Text analysis Visualization VOSviewer

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Impact of Finger Type in Contactless Fingerprint Verification

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Procedia computer science 2024年 246卷 3928-3937页

作者： Karama Abdeljabbar Islem Jarraya Tarek M. Hamdani Adel M. Alimi REGIM-Laboratory—REsearch Groups in Intelligent Machines National Engineering School of Sfax (ENIS) Sfax Tunisia Higher Institute of Computer Science Mahdia (ISIMa) University of Monastir Tunisia Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

Contactless fingerprint authentication has gained popularity as a field of research in biometrics in recent years. Unlike traditional fingerprint recognition systems that require direct contact of the person’s finger with the sensor, contactless fingerprint systems offer several advantages, among them ease of capture and cost-effectiveness. Despite the progress made in this field, poor contrast, background noise, and limited image information continue to pose difficulties for fingerprint recognition in contactless environments. Furthermore, the number of images in published fingerprint biometric datasets for each person is restricted, and there is insufficient data for efficient training. Nevertheless, Convolutional Neural Network (CNN) architectures have been widely used, necessitating large databases. To address these issues, this paper introduces a Siamese network designed for the purpose of identity Verification, using the contactless thumb fingerprint modality to enhance recognition results. The Siamese network is able to extract pertinent features from noisy images with low contrast and limited information, even if they have limited information and low contrast. Additionally, this work proposes the use of the contactless thumb fingerprint modality instead of the contactless index fingerprint modality, which is more commonly used in related works. Consequently, the Mobile FingerPrint (MFP) dataset is introduced and constructed for evaluation. Experimental results demonstrate the efficiency of the proposed method, achieving an accuracy of 98.68% for thumb fingerprint Verification.

关键词： Contactless Fingerprint Authentication Thumb Fingerprint Deep Learning Siamese Network Biometric Verification

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Design and Development of a Fixed-Wing Tail-Sitter Tethered VTOL Drone with Custom Autopilot and Robust Control Techniques 16th

Design and Development of a Fixed-Wing Tail-Sitter Tether...

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16th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Safaee, Ahmad Aguiar, A. Pedro Santos, Filipe Neves Moreira, Antonio Paulo SYSTEC-ARISE Research Center for Systems and Technologies Faculty of Engineering University of Porto Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering Technology and Science CRIIS-Centre for Robotics in Industry and Intelligent Systems Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering and DEEC/FEUP - Department of Electrical and Computer Engineering Faculty of Engineering of the University of Porto Porto4200-465 Portugal

ISBN: (纸本)9783031817236

The need for aerial platforms capable of sustained operation is critical in fields such as surveillance, agricultural monitoring, disaster response, and temporary telecommunication systems. Traditional unmanned aerial vehicles (UAVs) are hampered by limited battery capacity and inefficient energy utilization. This paper introduces an autonomous vertical flight drone tethered to a ground-based platform, which overcomes these limitations. The drone is designed to optimize its position and energy usage by harnessing wind currents, functioning similarly to a kite when conditions permit. This is enabled by a custom-designed autopilot system comprising both hardware and software tailored for this application. We detail the model identification process for three vertical take-off and landing (VTOL) drone systems, employing authentic data that measures lateral positional errors from the tether. Robust mixed sensitivity controllers are developed and implemented. The effectiveness of these controllers is validated through simulations showing their capability to handle uncertainties. Furthermore, the practical application and robust performance of the system are confirmed by a real-world case study, highlighting its potential in real operational environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： VTOL/STOL aircraft

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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Secure Communication in Dynamic RDARS-Driven systems

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IEEE Wireless Communications Letters 2025年

作者： Pei, Ziqian Wang, Jintao Zhang, Pingping Shi, Zheng Yang, Guanghua Ma, Shaodan University of Macau Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City Macau999078 China Jinan University School of Intelligent Systems Science and Engineering Zhuhai519070 China

In this letter, we investigate a dynamic reconfigurable distributed antenna and reflection surface (RDARS)-driven secure communication system, where the working mode of the RDARS can be flexibly configured. We aim to maximize the secrecy rate by jointly designing the active beamforming vectors, reflection coefficients, and the channel-aware mode selection matrix. To address the non-convex binary and cardinality constraints introduced by dynamic mode selection, we propose an efficient alternating optimization (AO) framework that employs penalty-based fractional programming (FP) and successive convex approximation (SCA) transformations. Simulation results demonstrate the potential of RDARS in enhancing the secrecy rate and show its superiority compared to existing reflection surface-based schemes. © 2012 IEEE.

关键词： Secure communication

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