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Zero-dynamics attack detection based on data association in feedback pathway

Cognitive Robotics 2025年 5卷 126-139页

作者： Zhang, Zeyu Li, Hongran Todo, Yuki Division of Electrical Engineering and Computer Science Kanazawa University Ishikawa Kanazawa9201192 Japan School of Computer Engineering Jiangsu Ocean University Jiangsu Lianyungang222005 China Faculty of Electrical Information and Communication Engineering Kanazawa University Ishikawa Kanazawa9201192 Japan

This paper considers the security of non-minimum phase systems, a typical kind of cyber-physical systems. Non-minimum phase systems are characterized by unstable zeros in their transfer functions, making them particularly susceptible to disturbances and attacks. The non-minimum phase systems are more vulnerable to zero-dynamics attack (ZDA) than minimum phase systems. ZDA is a stealthy attack strategy that exploits the internal dynamics of a system, remaining undetectable while causing gradual system destabilization. Recent cyber incidents have demonstrated the increasing risk of such hidden attacks in critical infrastructures, such as power grids and transportation systems. This paper first demonstrates that the existing ZDA has the limitation of falling into local convergence, and then proposes an enhanced zero-dynamics attack (EZDA), which overcomes local convergence by diverging system data. Furthermore, this paper presents an autoregressive model which can build the data association between the original data and the forged data. By observing the fluctuations in state values, the presented model can detect not only ZDA, but also EZDA. Finally, numerical simulations and an application example are provided to verify the theoretical results. © 2025

关键词： Cyber attacks

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Buffer and Splitter Insertion for Adiabatic Quantum-Flux-Parametron Circuits

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 975-988页

作者： Fu, Rongliang Wang, Mengmeng Kan, Yirong Chen, Olivia Yoshikawa, Nobuyuki Yu, Bei Ho, Tsung-Yi The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan Nara Institute of Science and Technology Division of Information Science Nara630-0192 Japan Kyushu University Department of Advanced Information Technology Fukuoka819-0395 Japan

The extremely low-bit energy characteristic of the adiabatic quantum-flux-parametron (AQFP) circuit makes it a promising candidate for highly energy-efficient computing systems. However, in contrast with conventional circuit design, general logic synthesis tools can not make sure that the circuit functionality of generated AQFP circuits is correct. AQFP circuits require buffer and splitter insertion for dataflow synchronization at all clock phases of the circuit and multifan-out driving. Notably, buffers and splitters inserted take up much area and delay in AQFP circuits, also causing a significant increase in energy dissipation. To address this problem, this article analyses in detail why buffer and splitter insertion is necessary for AQFP circuits and proposes a global optimization framework for this purpose. This framework consists of three parts: 1) logic level assignment;2) splitter tree generation;and 3) buffer insertion. An integer linear programming algorithm is proposed for the logic level assignment to estimate the globally optimal number of inserted buffers and splitters. Subsequently, a dynamic programming-based multiway search tree generation algorithm is proposed to construct an optimal splitter tree for each net of the input circuit. Moreover, three optimization strategies are proposed to further enhance the effectiveness and efficiency of our framework. Experimental results on ISCAS'85 and EPFL benchmarks demonstrate the effectiveness and efficiency of our proposed framework compared with the state-of-the-art, particularly with significant advantages on large circuits. © 2024 IEEE.

关键词： Global optimization

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FLSN-MVO: Edge Computing and Privacy Protection Based on Federated Learning Siamese Network With Multi-Verse Optimization Algorithm for Industry 5.0

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 3443-3458页

作者： Yin, Shoulin Li, Hang Laghari, Asif Ali Teng, Lin Reddy Gadekallu, Thippa Almadhor, Ahmad Shenyang Normal University Software College Shenyang110034 China Harbin Engineering University School of Information and Communication Engineering Harbin150000 China Lovely Professional University Division of Research and Development Phagwara144411 India Chitkara University Center of Research Impact and Outcome Rajpura140401 India Al Jouf University College of Computer and Information Sciences Department of Computer Engineering and Networks Sakaka72388 Saudi Arabia

With the development of deep learning technology, artificial intelligence has important applications in all aspects of society, but the lack of data has become a vital factor restricting the further evolution of artificial intelligence in industry 5.0. Federated learning can effectively use the edge node data and solve the data problem of artificial intelligence model training by sharing gradient. In federated learning, the terminal transmits the updated model parameter values instead of the primordial data to the server, thus becoming a key technology to ensure data privacy and security in edge computing. However, since attackers can use the shared gradient to launch malicious attacks to steal users’ privacy, how to securely upload the gradient and aggregate it has become an important topic to ensure privacy security in federated learning. Therefore, this paper proposes an edge computing and privacy protection based on federated learning Siamese network with multi-verse optimization algorithm for industry 5.0. This scheme can reduce the expenditure of endpoint participation in federated learning while protecting user privacy. In the federated learning structure, the feature information of the input samples is mapped to a new output vector through the subnetwork of the Siamese network, and the approximation degree between the input samples is judged by comparing the similarity degree between the output vectors. The parameters of the network are optimized by multi-verse optimization algorithm to reduce the convergence time. Meanwhile, an adaptive weight aggregation algorithm is designed to reduce the degradation of model performance and stability caused by data quality differences, so as to improve the accuracy of the model and accelerate the model to reach the optimal value. Finally, comprehensive experiments on three public standard data sets show that the proposed method achieves higher model accuracy and faster model convergence than the most advanced methods. ©

关键词： Federated learning

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On Strong Secrecy for Multiple Access Channels With States and Causal CSI

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IEEE Transactions on information Theory 2025年第4期71卷 3070-3099页

作者： Chen, Yiqi Oechtering, Tobias J. Skoglund, Mikael Luo, Yuan Technical University of Munich Chair of Theoretical Information Technology Munich80333 Germany Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China KTH Royal Institute of Technology Division of Information Science and Engineering School of Electrical Engineering and Computer Science Stockholm100 44 Sweden Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3 Shanghai200240 China

Strong secrecy communication over a discrete memoryless state-dependent multiple access channel (SD-MAC) with an external eavesdropper is investigated. The channel is governed by discrete memoryless and i.i.d. channel states, and the channel state information (CSI) is revealed to the encoders in a causal manner. The main results of this paper are inner and outer bounds of the capacity region, for which we investigate coding schemes incorporating wiretap coding and secret key agreements between the sender and the legitimate receiver. Two kinds of block Markov coding schemes are proposed. The first is a new coding scheme that uses backward decoding and the Wyner-Ziv coding, and the secret key is constructed from a lossy description of the CSI. The other is an extended version of an existing coding scheme for point-to-point wiretap channels with causal CSI. A numerical example shows that the achievable region given by the first coding scheme can be strictly larger than the second one. However, these two schemes do not outperform each other in general, and there exist some numerical examples in which each coding scheme achieves some rate pairs that cannot be achieved by another scheme. Our established inner bound reduces to some best-known results in the literature as special cases. We further investigate some capacity-achieving cases for state-dependent multiple access wiretap channels (SD-MAWCs) with degraded message sets. It turns out that the two coding schemes are both optimal in these cases. © 1963-2012 IEEE.

关键词： Markov processes

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Safe Model Predictive Control of a Non-Holonomic Mobile Manipulator Under Multiple Constraints using Control Barrier Functions

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IEEE Access 2025年 13卷 76638-76653页

作者： Kashi, Zahra Yadegar, Meysam Meskin, Nader Cassandras, Christos G. Qom University of Technology Electrical and Computer Engineering Qom Iran Qatar University Electrical Engineering Doha Qatar Boston University Division of Systems Engineering Center for Information and Systems Engineering BostonMA02446 United States

This paper introduces a novel control strategy to ensure safety in the navigation of a mobile manipulator comprising a fixed-base manipulator mounted on a mobile platform. The approach initially addresses the trajectory tracking problem of a non-holonomic mobile manipulator (NH-MM) by employing decoupling dynamic control through model predictive control-optimizable control barrier function (MPCOCBF). This allows for independent control of the end-effector and mobile platform trajectories, obstacle avoidance, and simultaneously adjusting the joint and control input limitations. The objective is to leverage the system's redundancy to enable the mobile platform to effectively navigate feasible obstacle avoidance scenarios without affecting the primary task performance of the end-effector. Additionally, the method aims to perform obstacle avoidance when the redundancy of the manipulator is insufficient, addressing non-feasible obstacle avoidance scenarios. Through this approach, the mobile manipulator effectively avoids obstacles, allowing the end-effector to autonomously carry out its intended task. The effectiveness of the proposed method is validated through simulations and comparison with existing approaches. Additionally, a quantitative analysis is provided to evaluate and compare the performance of the controllers. © 2013 IEEE.

关键词： Predictive control systems

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Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models

IEEE Open Journal of the Computer Society

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IEEE Open Journal of the computer Society 2025年第1期6卷 574-585页

作者： Hameed, Saad Qolomany, Basheer Belhaouari, Samir Brahim Abdallah, Mohamed Qadir, Junaid Al-Fuqaha, Ala Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha5825 Qatar Howard University Department of Medicine College of Medicine WashingtonDC20059 United States Qatar University Department of Computer Science and Engineering Doha5825 Qatar

Determining the ideal architecture for deep learning models, such as the number of layers and neurons, is a difficult and resource-intensive process that frequently relies on human tuning or computationally costly optimization approaches. While Particle Swarm Optimization (PSO) and Large Language Models (LLMs) have been individually applied in optimization and deep learning, their combined use for enhancing convergence in numerical optimization tasks remains underexplored. Our work addresses this gap by integrating LLMs into PSO to reduce model evaluations and improve convergence for deep learning hyperparameter tuning. The proposed LLM-enhanced PSO method addresses the difficulties of efficiency and convergence by using LLMs (particularly ChatGPT-3.5 and Llama3) to improve PSO performance, allowing for faster achievement of target objectives. Our method speeds up search space exploration by substituting underperforming particle placements with best suggestions offered by LLMs. Comprehensive experiments across three scenarios—(1) optimizing the Rastrigin function, (2) using Long Short-Term Memory (LSTM) networks for time series regression, and (3) using Convolutional Neural Networks (CNNs) for material classification—show that the method significantly improves convergence rates and lowers computational costs. Depending on the application, computational complexity is lowered by 20% to 60% compared to traditional PSO methods. Llama3 achieved a 20% to 40% reduction in model calls for regression tasks, whereas ChatGPT-3.5 reduced model calls by 60% for both regression and classification tasks, all while preserving accuracy and error rates. This groundbreaking methodology offers a very efficient and effective solution for optimizing deep learning models, leading to substantial computational performance improvements across a wide range of applications. © 2020 IEEE.

关键词： Deep learning

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Personal data sharing model with legal validity in blockchain-based secure environment 27

Personal data sharing model with legal validity in blockchai...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Shin, Su Jin Shin, Sang Uk Department of Information Security Graduate School Pukyong National University Korea Republic of Division of Computer Engineering and Artificial Intelligence Pukyong National University Korea Republic of

ISBN: (纸本)9791188428137

This paper proposes a data sharing model that ensures the legal validity of data provided by individuals as legal evidence within a blockchain environment. The proposed model applies Distributed Pseudo-Random Functions (DPRFs) to prevent all participants, except the data requester, from obtaining the original data. Based on these characteristics, the model guarantees integrity, confidentiality, and non-repudiation, thereby ensuring that data provided by individuals can possess legal validity as evidence. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Data integrity

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Breast Cancer Detection using Domain-Adversarial Training (DANN) with Invariant Risk Minimization (IRM) Hybrid Approach 3

Breast Cancer Detection using Domain-Adversarial Training (D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Akash, K.V. Daniel, Esther Seetha, S. Durga, S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Cmr Institute of Technology Department of Information Science & Engineering India Amrita Vishwa Vidyapeetham Tifac Core in Cyber Security Amrita School of Engineering India

ISBN: (纸本)9798331509675

The breast cancer detection performs a key function in the health care network. The precise and early detection of cancer in the breast could aid to save life of the sufferer. The traditional machine learning methods struggle due to the data used to train is different from data used later this is known as Domain shifts. This project uses a hybrid model known as Domain-Adversarial Training of Neural Networks (DANN) with Invariant Risk Minimization (IRM) Hybrid Method to identification of the tumor in the breast. Through integrating the (DANN) and IRM works perfectly among the various type of data and also with the various image from the various sources. This model is more precise and reliable than any other older *** that this model act as an effective tool for detecting the breast cancer in the early stages. © 2025 IEEE.

关键词： Adversarial machine learning

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Invoking an Efficient Deep Learning Approach for Real-Time Detection of False Data Injection Attacks

Invoking an Efficient Deep Learning Approach for Real-Time D...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Eddin, Maymouna Ez Massaoudi, Mohamed Abu-Rub, Haitham Shadmand, Mohammad Abdallah, Mohamed Bcgovic, Miroslav Ghrayeb, Ali Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States Hamad Bin Khalifa University Information and Computing Technology Division Doha Qatar University of Illinois at Chicago Department of Electrical and Computer Engineering Chicago United States

ISBN: (纸本)9798331541125

With the increasing complexity of power systems and their vulnerability to cyber-attacks, robust detection mechanisms are crucial for ensuring the grid's transient and steady-state stability and resiliency. This study introduces an efficient deep learning framework to detect false data injection attacks (FDIAs) in power systems. Leveraging the MATPOWER environment, we simulate a realistic power grid scenario to generate datasets comprising normal operations and various FDIA scenarios. Utilizing advanced deep learning techniques, including convolutional neural networks enhanced with channel attention mechanisms and transformer encoders, our model processes sequential power system data to discern subtle attack signatures from normal fluctuations. The proposed methodology employs a stratified k-fold cross-validation approach on resampled datasets to address class imbalance, ensuring robust model evaluation. The model's performance is meticulously assessed using accuracy metrics and confusion matrices, demonstrating its capability to effectively identify and differentiate between normal and compromised states in power systems. Results on the IEEE 39-bus and 300-bus systems highlight that this approach significantly enhances detection capabilities, achieving high accuracy and practical detection times of less than 2 milliseconds for the IEEE 39-bus system, which addresses the real-time requirements. These promising results suggest that such models can be pivotal for preemptive security measures and real-time monitoring of power grids. © 2025 IEEE.

关键词： Convolutional neural networks

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