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Data-driven output regulation control for constrained linear systems

science China(information sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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An intrusion attack classification using bio-inspired optimization technique and ensemble learning model for edge computing environments

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Multimedia Tools and Applications 2024年 1-28页

作者： Rahamathulla, Mohemmed Yousuf Ramaiah, Mangayarkarasi School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamilnadu Vellore India

The phenomenal rise in network traffic across various sectors, driven by advancements in network communication, has led to an explosion of connected devices. While internet-based service providers have enhanced smart environments, these environments remain highly vulnerable to security risks. Due to their resource- constrained nature, Internet of Things (IoT) systems rely on edge servers for processing. Cutting-edge machine learning algorithms have significantly improved the security of edge computing systems by enabling early intrusion detection. However, the dynamic nature of our society demands more sophisticated Network Intrusion Detection Systems (NIDS) that can balance the challenge of managing the explosive growth of network traffic data with effectively responding to attacks. This paper proposes a novel NIDS that can train and learn from massive amounts of data in a centralized environment. To identify the best subset of features for improved data representation and classification, a bio-inspired algorithm, the Improved Firefly Optimization Algorithm (IFOA), is employed. Furthermore, a hyper-tuned Extremely Randomized Trees (ERT) attack detection model is developed to accurately identify malicious behavior while minimizing false positives. The experimental results demonstrate that the proposed approach achieves superior accuracy and execution speed compared to alternative solutions and its baseline versions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Network intrusion

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Computation of Graph Fourier Transform Centrality Using Graph Filter

IEEE Open Journal of Circuits and Systems

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IEEE Open Journal of Circuits and Systems 2024年 5卷 69-80页

作者： Tseng, Chien-Cheng Lee, Su-Ling National Kaohsiung University of Science and Technology Department of Computer and Communication Engineering Kaohsiung824005 Taiwan Chang Jung Christian University Department of Computer Science and Information Engineering Tainan711301 Taiwan

In this paper, the computation of graph Fourier transform centrality (GFTC) of complex network using graph filter is presented. For conventional computation method, it needs to use the non-sparse transform matrix of graph Fourier transform (GFT) to compute GFTC scores. To reduce the computational complexity of GFTC, a linear algebra method based on Frobenius norm of error matrix is applied to convert the spectral-domain GFTC computation task to vertex-domain one such that GFTC can be computed by using polynomial graph filtering method. There are two kinds of designs of graph filters to be studied. One is the graph-aware method;the other is the graph-unaware method. The computational complexity comparison and experimental results show that the proposed graph filter method is more computationally efficient than conventional GFT method because the sparsity of Laplacian matrix is used in the implementation structure. Finally, the centrality computations of social network, metro network and sensor network are used to demonstrate the effectiveness of the proposed GFTC computation method using graph filter. © 2020 IEEE.

关键词： Complex networks

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IaaSI: a device based interoperability as a service for IoMT devices

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14321-14332页

作者： Kotha, Aditya Manohar, Kota U, Venkanna Department of Computer Science and Engineering International Institute of Information Technology Naya Raipur India

Interoperability is a crucial aspect of the effective functioning of Internet of Things (IoT) devices, particularly in the healthcare industry. Although the use of IoT devices in healthcare has brought numerous benefits, such as remote sensing, monitoring, and data analysis, it has also introduced new challenges, notably in the area of interoperability. The lack of semantic and syntactic interoperability has hindered the ability of these devices to communicate and share data, leading to inefficiencies and limitations in their use. To address these challenges, this study proposes a solution that employs natural language processing (NLP) techniques to enhance the efficiency and effectiveness of healthcare IoT. Specifically, the solution utilizes Bidirectional Encoder Representations from Transformers (BERT) based string matching and Fuzzy Inference System (FIS) to facilitate data correlation with an existing vocabulary and a parser. The proposed approach was evaluated with real-world data from healthcare IoT devices, yielding an accuracy of 85.71% and an average processing delay of 0.46 s, thus demonstrating the potential of natural language processing techniques to enhance the interoperability of healthcare IoT devices. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Fuzzy logic

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A Pre-Selection-Based Ant Colony System for Integrated Resources Scheduling Problem at Marine Container Terminal

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computers, Materials & Continua 2024年第8期80卷 2363-2385页

作者： Rong Wang Xinxin Xu Zijia Wang Fei Ji Nankun Mu School of Electronic and Information Engineering South China University of TechnologyGuangzhou510641China School of Computer Science and Technology Ocean University of ChinaQingdao266100China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China School of Computer Science Chongqing UniversityChongqing400044China

Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics ***,the efficiency of resource scheduling significantly influences the operation performance of *** solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm ***,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated *** new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the ***,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection *** the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible *** the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality *** evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is *** results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms.

关键词： Resource scheduling problem(RSP) ant colony system(ACS) marine container terminal(MCT) pre-selection strategy

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DedupChain: A secure Blockchain-enabled Storage System with Deduplication for Zero-Trust Network

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2070-2086页

作者： Qi, Saiyu Wang, Qiuhao Wei, Wei Yang, Xu Zhao, Hongguang Liu, Yuhao Yang, Xu Qi, Yong Xi'an Jiaotong University School of Computer Science and Technology China Xi'an University of Technology School of Computer Science and Engineering China Alibaba Group China Northwestern Polytechnical University School of Electronics and Information China

Permissioned blockchain is a promising methodology to build zero-trust storage foundation with trusted data storage and sharing for the zero-trust network. However, the inherent full-backup feature of the permissioned blockchain poses potential data privacy risks and substantial storage costs, hindering its usage as a storage medium. These issues necessitate the usage of secure data deduplication technology to mitigate them. Unfortunately, current secure data deduplication schemes are predominantly designed with centralized cloud servers in mind and are not suitable for distributed blockchain systems. The reason is that the full backup feature of the permissioned blockchain renders a wide attack surface to offline brute-force and frequency analysis attacks. In response, we propose DedupChain, a secure blockchain-enabled storage system with deduplication for zero-trust networks. DedupChain employs a trusted execution environment (i.e., Inter SGX enclave) in conjunction with Oblivious RAM (ORAM) to offer a novel security guarantee named oblivious data deduplication, which empowers DedupChain with the ability to defend offline brute-force and frequency analysis attacks. DedupChain also proposes several novel techniques to address the security and efficiency issues raised by the SGX enclave. We implemented a system prototype of DedupChain and evaluated its performance metrics. Our experimental results show that DedupChain exhibits satisfactory operational delays, throughput, and storage overhead. Security analysis shows that DedupChain is robust enough to withstand several types of attacks. To the best of our knowledge, we are the first to apply secure data deduplication techniques to address data privacy and storage cost issues raised by permissioned blockchain when used as a zero-trust storage medium. © 1983-2012 IEEE.

关键词： Blockchain

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Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge Computing for Makespan Optimization

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Tsinghua science and technology 2024年第3期29卷 806-817页

作者： Lei Zeng Qi Liu Shigen Shen Xiaodong Liu School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Information Engineering Huzhou UniversityHuzhou 313000China School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the *** management of experience data are also modified to fully utilize its value and improve learning *** learning agents usually learn from an ignorant state,which is *** such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task *** optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition *** proposed algorithm is compared with six other methods in simulation *** show that the proposed algorithm outperforms other benchmark methods regarding makespan.

关键词： edge computing task scheduling reinforcement learning makespan Double Deep Q Network(DQN)

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Social issues of aging: Immersive technology solutions

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IEEE Potentials 2023年第6期42卷 12-17页

作者： Huang, Xiang-Rui Chen, Liang-Bi National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan

With the advancement of medical care and technology, human life expectancy is increasing, many advanced countries have aging societies, and the elderly have increasing needs for society to address;these have become some of the main problems to be solved in modern society (Chang et al., 2021). To solve these problems, long-term care institutions have become one of the solutions. In addition, dementia is the most important social problem among elderly individuals (Tsang et al., 2020). In most Asian countries, the solution to the problem of dementia in the elderly is to establish long-term care institutions, such as villages for elderly individuals and nursing homes. However, there is still the problem of companionship in an aging society. The needs of the elderly lie in companionship. Through family companionship, the elderly can share the joys of daily life with their families. © 1988-2012 IEEE.

关键词： Medical problems

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OSSIoT: An ontology-based Operational Security model for Social Internet of Things using Machine Learning Techniques

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第10期51卷 1440-1453页

作者： Kumar, K.S. Santhosh Hanumanthappa, J. Prakash, S.P. Shiva Krinkin, Kirill Department of Studies in Computer Science University of Mysore Karnataka Mysuru India Department of Information Science and Engineering JSS Science and Technology University Karnataka Mysuru India School of Computer Science and Engineering Constructor University Bremen Germany

The Social Internet of Things (SIoT) is an innovative fusion of IoT and smart devices that enable them to establish dynamic relationships. Securing sensitive data in a smart environment requires a model to determine the relationships between devices through object profiling and ontological models. To address this need, we have proposed an ontology-based operational security model for the SIoT. In our approach, the interpolation method is used to establish relationships, while fiduciary relationships are employed to detect threats. Furthermore, the encryption of heterogeneous device data, coupled with the implementation of an operation-based intrusion detection system, proves highly effective in identifying potential *** relationships are identified as intruders and validated using machine learning techniques. Encrypting heterogeneous device data, along with an operation-based intrusion detection system, efficiently identifies threats in the ever-evolving dynamic nature of the SIoT environment. the encryption of heterogeneous device data, coupled with an operation-based intrusion detection system, effectively identifies threats. Invalid relationships are promptly identified as attackers and machine learning techniques are used to validate relationships encryption and machine learning stand as indispensable tools in the endeavor to secure sensitive information within the realm of the SIoT. The suggested model outperformed the results of the current model, as evidenced by its average accuracy of 85.67%, precision of 90.37%, recall of 92.06%, and F1-score of 91.03% when compared to the existing model. © (2024), (International Association of Engineers). All rights reserved.

关键词： Network intrusion

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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