Article
Automation & Control Systems
Xiao-Guang Zhang, Guang-Hong Yang
Summary: This article investigates the trade-off problem between attack stealthiness and attack performance in cyber-physical systems. It proposes an offline stealthy attack strategy and analyzes the upper bound of attack performance for stable systems. The theoretical results are demonstrated through simulations and experiments.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Automation & Control Systems
Yingwen Zhang, Zhaoxia Peng, Guoguang Wen, Jinhuan Wang, Tingwen Huang
Summary: This article studies the impact of constrained optimal stealthy attacks on the state estimator, proposing a novel resource-constrained attack model and solving the optimization problem using the Lagrange multiplier method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xiuming Liu, Edith C. -H. Ngai, Jiangchuan Liu
Summary: In this paper, the distributed state estimation problem under false data injection attack in modern distributed cyber-physical systems is studied. An integrated detection + fusion solution based on Kullback-Leibler divergences is proposed, avoiding the exchange of raw sensor data. The impact of network connectivity on empirical detection error rate and state estimation accuracy is discussed with simulation results presented for the integrated solution.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2021)
Article
Automation & Control Systems
Jun Shang, Donny Cheng, Jing Zhou, Tongwen Chen
Summary: This article studies the vulnerability of measurement and control channels in closed-loop systems, analyzing optimal stealthy attacks on linear quadratic Gaussian (LQG) control. Different types of stealthiness are considered, with worst-case attacks on two different channels compared.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2022)
Article
Automation & Control Systems
Arunava Naha, Andre M. H. Teixeira, Anders Ahlen, Subhrakanti Dey
Summary: This paper proposes a parsimonious policy to limit the average number of watermarking events when the attack is not present, which reduces the control cost in a networked control system. The system is modeled as a stochastic optimal control problem, and dynamic programming is used to minimize the average detection delay. The optimal solution results in a two threshold policy on the posterior probability of attack, and asymptotically approximate analytical expressions are derived for the detection delay and false alarm rate.
Article
Automation & Control Systems
Xiu-Xiu Ren, Guang-Hong Yang
Summary: This article focuses on designing optimal stealthy attack strategies for cyber-physical systems modeled by LQG dynamics, introducing strictly stealthy and epsilon-stealthy attacks. Achieving higher attack performance often requires sacrificing stealthiness level. An optimal epsilon-stealthy attack is designed to achieve the upper bound of attack performance, different from existing suboptimal approaches for LQG systems, and simulations are provided to verify the results.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Engineering, Multidisciplinary
Prem Shankar Kumar, L. A. Kumaraswamidhas, S. K. Laha
Summary: The paper introduces a novel health degradation indicator for machineries based on Kullback-Leibler divergence to assess degradation in rolling element bearings and predict remaining useful life. The confidence value is proposed for prognostics approach and can quantify different degradation stages. Gaussian Process Regression is utilized to forecast degradation trend in bearings, remove outliers, and estimate remaining useful life accurately.
Article
Multidisciplinary Sciences
Xiuqiong Chen, Jiayi Kang, Mina Teicher, Stephen S. -T. Yau
Summary: The study introduces a new linear regression Kalman filter for discrete nonlinear filtering problems. By minimizing the Kullback-Leibler divergence to capture information of the reference density, the method requires fewer samples to evolve the conditional densities deterministically, leading to higher efficiency compared to other widely used filtering methods.
Article
Automation & Control Systems
Chi Wei, Shaobin Huang, Rongsheng Li, Ye Liu, Naiyu Yan
Summary: This paper proposes a fusion scheme to correct spelling errors in sentences, which utilizes a detection module, original input, and masked input to acquire comprehensive sentence semantic information, achieving superior performance on two benchmarks.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kaijing Jin, Dan Ye
Summary: This article investigates the optimal innovation-based attack strategy for networked linear quadratic Gaussian (LQG) systems. The attacks need to follow strict stealthiness or ε-stealthiness described by the Kullback-Leibler divergence in order to bypass the detector. The attackers aim to increase the quadratic control cost and decrease the attack cost, which is formulated as a nonconvex optimization problem. The nonconvex objective function is transformed into a linear function related to attack matrices and covariance matrices of the tampered innovations based on the cyclic property of the matrix trace. The optimal strictly stealthy attack is obtained using the matrix decomposition technique. Furthermore, the optimal ε-stealthy attack is derived to achieve a higher-attack effect through an integrated convex optimization.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Yi-Gang Li, Guang-Hong Yang, Xiangdong Wang
Summary: This paper investigates the optimal deception attacks on multiple channels in cyber-physical systems under energy constraints. A fusion attack model is proposed to fully utilize eavesdropped data by linearly combining innovations from different channels. The state estimation error is quantified and analyzed using statistical characteristics and the orthogonality principle. The attack parameters and energy allocation strategy are derived through a two-step process without sacrificing optimality, solving a multivariate semi-definite programming problem and a linear 0-1 programming problem, respectively. Simulation examples are provided to demonstrate the effectiveness of the proposed method.
Article
Remote Sensing
Elena Basan, Alexandr Basan, Alexey Nekrasov, Colin Fidge, Nikita Sushkin, Olga Peskova
Summary: This article presents a method for detecting cyber security attacks that spoof the GPS signal of a UAV. The method reduces the need for large amounts of data and training time, simplifying the process of creating an anomaly detection system.
Article
Engineering, Mechanical
Tulay Ercan, Omid Sedehi, Lambros S. Katafygiotis, Costas Papadimitriou
Summary: An optimal sensor placement (OSP) framework for virtual sensing using the augmented Kalman Filter (AKF) technique is proposed based on information and utility theory. The framework considers uncertainties in the structural model and modelling error parameters, and maximizes the utility function through heuristic sequential sensor placement (SSP) strategies and genetic algorithms (GA). The study highlights the importance of accounting for robustness to errors and uncertainties in selecting the optimal sensor configuration using a Finite Element (FE) model.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Automation & Control Systems
Yue Cao, Nabil Magbool Jan, Biao Huang, Mengqi Fang, Yalin Wang, Weihua Gui
Summary: This paper proposes a multimodal process monitoring method using VBPCA and KL divergence to capture multimodal process information and fuse statistics and control limits through Bayesian inference to obtain the final monitoring result by considering latent and noise statistics.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2021)
Article
Automation & Control Systems
Qirui Zhang, Kun Liu, Andre M. H. Teixeira, Yuzhe Li, Senchun Chai, Yuanqing Xia
Summary: This article investigates the design of online stealthy attacks, where the attacker estimates the system's state with intercepted data and computes the optimal attack accordingly. The Kullback-Leibler divergence is used to ensure stealthiness. The attacker should solve a convex optimization problem at each instant to compute the attack parameters.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Computer Science, Theory & Methods
Rohan Tabish, Renato Mancuso, Saud Wasly, Rodolfo Pellizzoni, Marco Caccamo
Article
Chemistry, Analytical
Ali M. Albishi, Seyed H. Mirjahanmardi, Abdulbaset M. Ali, Vahid Nayyeri, Saud M. Wasly, Omar M. Ramahi
Article
Automation & Control Systems
Li-Ning Liu, Guang-Hong Yang, Saud Wasly
Summary: This article investigates the coordination dual-mode energy management problem for a microgrid, proposing a novel distributed algorithm that adaptively responds to mode switching and obtains optimal operation in a distributed way. Compared with existing algorithms, this algorithm has a predefined convergence time and an event-triggered communication strategy, greatly reducing communication resource consumption.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Saud Wasly
Summary: This paper introduces a system-level hardware modeling and simulation framework based on Python with an emphasis on ease of use, utilizing meta-tracing JIT compilation techniques to improve simulation speed. The framework supports modeling components of different accuracies and combines time-driven simulation with transaction-level modeling to enable precise timing estimates.
2020 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT AND ADVANCED SYSTEMS (ICIAS)
(2021)
Proceedings Paper
Computer Science, Theory & Methods
Tushar Garg, Saud Wasly, Rodolfo Pellizzoni, Nachiket Kapre
PROCEEDINGS OF THE 2019 ACM/SIGDA INTERNATIONAL SYMPOSIUM ON FIELD-PROGRAMMABLE GATE ARRAYS (FPGA'19)
(2019)
Proceedings Paper
Computer Science, Hardware & Architecture
Saud Wasly, Rodolfo Pellizzoni
25TH IEEE REAL-TIME AND EMBEDDED TECHNOLOGY AND APPLICATIONS SYMPOSIUM (RTAS 2019)
(2019)
Proceedings Paper
Computer Science, Information Systems
Saud Wasly, Rodolfo Pellizzoni, Nachiket Kapre
2017 INTERNATIONAL CONFERENCE ON FIELD PROGRAMMABLE TECHNOLOGY (ICFPT)
(2017)
Proceedings Paper
Computer Science, Hardware & Architecture
Rohan Tabish, Renato Mancuso, Saud Wasly, Sujit S. Phatak, Rodolfo Pellizzoni, Marco Caccamo
PROCEEDINGS OF THE 23RD IEEE REAL-TIME AND EMBEDDED TECHNOLOGY AND APPLICATIONS SYMPOSIUM (RTAS 2017)
(2017)
Proceedings Paper
Computer Science, Hardware & Architecture
Ahmed Alhammad, Saud Wasly, Rodolfo Pellizzoni
21ST IEEE REAL-TIME AND EMBEDDED TECHNOLOGY AND APPLICATIONS SYMPOSIUM (RTAS 2015)
(2015)
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)
