Article
Automation & Control Systems
Wenshun Lv, Ju H. Park, Junwei Lu, Runan Guo
Summary: This paper proposes an adaptive fuzzy output feedback control scheme for addressing a class of unknown nonlinear systems with sensor attacks. Fuzzy logic systems are utilized to approximate the uncertain nonlinearities, and the backstepping technique is employed to construct controllers. By using a Nussbaum function to handle the unknown output feedback coefficient in the first step of backstepping design, an adaptive controller is developed to accommodate sensor attacks. Compared to current control schemes, the proposed adaptive controller can be applied to nonlinear systems with both uncertainty and unmeasurable states. Two examples are provided to validate the effectiveness of the proposed control scheme.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Engineering, Mechanical
Fan Yang, Zhou Gu, Shen Yan
Summary: This study addresses the event-based control of nonlinear cyber-physical systems subject to deception attacks. An improved Takagi-Sugeno fuzzy model is employed to solve the mismatch problem between the fuzzy system and fuzzy controllers. A novel queuing model is constructed to depict deception attacks and a switched event-based communication scheme dynamically converts with different attack modes. The effectiveness of the proposed approach is well verified on a mass-spring-damper system.
NONLINEAR DYNAMICS
(2021)
Article
Computer Science, Artificial Intelligence
Yannan Bi, Tong Wang, Jianbin Qiu, Min Li, Chunling Wei, Li Yuan
Summary: This article discusses the adaptive secure control problem for a class of uncertain nonlinear large-scale cyber-physical systems (CPSs) subjected to deception attacks. A novel adaptive fuzzy control scheme is designed to mitigate the effects of cyberattacks, while a Nussbaum function is proposed to handle unknown time-varying control directions. Furthermore, a finite-time converging secure control scheme is developed to ensure that compromised CPSs converge to a predetermined small set in finite time. It is shown that the proposed control scheme guarantees the semiglobal boundedness of all the signals in the closed-loop system. Two simulation examples are provided to demonstrate the effectiveness of the proposed secure control method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Jiali Ma, Jiaqi Wang, Shengyuan Xu, Shumin Fei, Hongyan Feng
Summary: This article considers the event-triggered-based adaptive finite-time secure control problem for nonlinear cyber-physical systems (CPSs) in the presence of unknown sensor and actuator deception attacks. A novel coordinate transformation and adaptive controller combined with a triggering mechanism are proposed. An adaptive switching law is also introduced to regulate the dynamic controller parameter, effectively compensating for the unknown deception attacks.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Mathematics, Interdisciplinary Applications
Yajing Ma, Zhanjie Li, Xiangpeng Xie, Dong Yue
Summary: This paper addresses the consensus problem of a type of uncertain switched nonlinear multi-agent systems under unknown system parameters and sensor deception attacks. By introducing novel auxiliary variables and adaptive laws, the damage to data caused by attacks and the uncertainty in the control process are resolved. Experimental results demonstrate the effectiveness of the proposed approach.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Automation & Control Systems
Xiao Wang, Ping Zhao
Summary: This article addresses the problem of adaptive control against deception attacks for switched nonlinear cyber-physical systems (CPSs) with more general and unknown nonlinearities. An adaptive controller is designed to mitigate the impact of state-dependent sensor attacks and input-dependent actuator attacks. Nussbaum-type functions are introduced to handle the unknown time-varying gains caused by these attacks. The effectiveness of the proposed approach is demonstrated through a simulation example.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Yue Zhao, Xin Du, Chunjie Zhou, Yu-Chu Tian, Xiaoya Hu, Daniel E. Quevedo
Summary: This article introduces an idea to address both actuator attacks and sensor attacks in cyber-physical systems (CPSs) and simplifies the mathematical modeling of attacked CPSs by using feedback linearization control, thus reducing the difficulty of resilient controller design.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Jing Xie, Shiyu Yan, Dong Yang
Summary: This paper addresses the finite-time adaptive resilient control problem for a class of switched nonlinear systems with deception attacks using the dynamic surface control (DSC) strategy. A more general class of uncertain nonstrict-feedback switched nonlinear systems with sensor and actuator deception attacks is considered. The proposed control strategy uses the approximation technique of neural networks (NNs) to deal with unknown nonlinear terms and compensate for sensor and actuator deception attacks.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Engineering, Electrical & Electronic
Wei Wang, Zhen Han, Kexin Liu, Jinhu Lu
Summary: This paper investigates the formation control problem for a group of nonholonomic mobile robots with unknown parameters and deception attacks. Distributed estimators and adaptive laws are designed to handle attack-induced uncertainties, and a novel distributed resilient formation control scheme is proposed. Experimental results validate the theoretical studies.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2021)
Article
Automation & Control Systems
Elham Akbari, Seyyed Mostafa Tabatabaei, Mojtaba Barkhordari Yazdi, Mohammad Mehdi Arefi, Jinde Cao
Summary: In this study, the tracking control problem is investigated for a switched time-delay nonlinear nonstrict-feedback cyber-physical system (CPS). These CPSs are susceptible to collusive network attacks, such as false data injection (FDI) and denial-of-service (DoS) attacks. To address these issues, a Lyapunov-Krasovskii (LK) candidate, a neural network (NN) switched observer, and a Nussbaum gain approach are employed. The proposed resilient control ensures bounded closed-loop system signals and tracking errors. Practical and numerical examples are presented to demonstrate the effectiveness of the proposed scheme.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Youngjun Joo, Zhihua Qu, Toru Namerikawa
Summary: This article presents an attack-resilient control structure for enhancing the security of a cyber-physical system against stealthy system integrity attacks. The proposed structure can detect stealthy attacks and maintain nominal performance, while using chaotic oscillators for secure communication to prevent eavesdropping of transmitted signals. Resilience against malicious attacks and robustness under time delay and nonlinear components of the CPS structure are investigated, and simulations for validation are performed on a quadruple-tank process.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Mathematics, Applied
Haibin Sun, Yahui Cui, Linlin Hou, Kaibo Shi
Summary: This paper discusses the application of adaptive finite-time control in cyber-physical systems subject to injection and deception attacks. By using the techniques of adding a power integrator and Nussbaum function, an adaptive finite-time controller is built to ensure that the system states converge to a neighborhood of the origin in finite-time.
APPLIED MATHEMATICS AND COMPUTATION
(2022)
Article
Automation & Control Systems
A. H. Tahoun, M. Arafa
Summary: This paper introduces the state-/output-feedback control approach for multi-channel nonlinear cyber-physical systems, considering various cyber-attacks. A new observer and an anti-cyber-attacks controller are proposed to deal with the effects of these attacks. Simulation results confirm the effectiveness of the proposed secure control approach.
Article
Physics, Multidisciplinary
Zhihao Chen, Xin Wang, Ning Pang, Yushan Shi
Summary: This paper focuses on the adaptive control problem of uncertain time-delay nonlinear cyber-physical systems with unknown time-varying deception attacks and full-state constraints. It proposes a new backstepping control strategy based on compromised variables and uses dynamic surface techniques to address the computational effort issue. The paper also introduces attack compensators, barrier Lyapunov function, radial basis function neural networks, and Lyapunov-Krasovskii function to improve control performance and handle unknown terms and time-delay issues.
Article
Automation & Control Systems
Yue Zhao, Chunjie Zhou, Yu-Chu Tian, Jianhui Yang, Xiaoya Hu
Summary: Cyber attacks pose serious threats to the security of cyber-physical systems. This article proposes a resilient control scheme based on cloud computing environments to address the destructive changes in system structure caused by actuator attacks. The scheme consists of a local resilient controller and a cloud-based resilient controller. Simulation experiments on a permanent synchronous motor control system demonstrate the effectiveness of the proposed scheme.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
