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A Vertical Federated Multiview Fuzzy Clustering Method for Incomplete data

IEEE Transactions on Fuzzy Systems 2025年第5期33卷 1510-1524页

作者： Li, Yan Hu, Xingchen Yu, Shengju Ding, Weiping Pedrycz, Witold Kiat, Yeo Chai Liu, Zhong National University of Defense Technology Laboratory for Big Data and Decision Changsha410073 China National University of Defense Technology School of Computer Changsha410073 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China Gliwice44-100 Poland University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Nanyang Technological University College of Computing and Data Science 639798 Singapore

Multiview fuzzy clustering (MVFC) has gained widespread adoption owing to its inherent flexibility in handling ambiguous data. The proliferation of privatization devices has driven the emergence of new challenge in MVFC research works. Federated learning, a technique that can jointly train without directly using raw data, has gain significant attention in decentralized MVFC. However, their applicability depends on the assumptions of data integrity and independence between different views. In fact, while within distributed environments, data typically exhibits two challenging problems: 1) multiple views within a single client;2) incomplete data. Existing methods exhibit limitations in effectively addressing these challenges. Hence, in this study, we aim at achieving the effective clustering for incomplete data by a novel vertical federated MVFC framework. Specifically, a unified clustering framework is designed to capture both local client learning and global server training. For the local client learning, the data reconstruction strategy and prototype alignment strategy are introduced to ensure the preservation of data structure and refinement of clustering relationships, which mitigates the impact of incomplete data. Meanwhile, the global training process implements aggregation based on client-specific information. The whole process is realized based on the unified fuzzy clustering framework, promoting collaborative learning between client-specific and server information. Theoretical analyses and extensive experiments are carefully conducted to validate the effectiveness and efficiency of the proposed method from multiple perspectives. © 1993-2012 IEEE.

关键词： data integrity

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Efficient Modeling Attack on Multiplexer PUFs via Kronecker Matrix Multiplication

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Wang, Hongfei Wan, Caixue Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

The Physical Unclonable Function (PUF) is valued for its lightweight nature and unique functionality, making it a common choice for securing hardware products requiring authentication and key generation mechanisms. In response to the susceptibility of individual PUFs to modeling attacks, advanced PUF variants have been developed to improve security measures. One notable type in this regard is the multiplexer-based composition of arbiter PUFs, known as MPUF, which aims to meet high reliability and security standards simultaneously. Current research on attacking MPUF encounters challenges such as substantial demands for training CRPs and low success rates. In this work, we propose a novel numerical modeling attack strategy for MPUFs. Using Kronecker products from mathematical perspective, this method precisely describe the MPUF model without using complex network architectures, boosting attack accuracy and overall efficiency. Experiment comparison with state-of-the-art works demonstrates that our method achieves better performance in terms of attack accuracy, robustness, and efficiency. Our method is able to successfully attack a (512, 8)-MPUF in 32.71 minutes with 97.14% accuracy, outperforming all existing attack methods on MPUFs. More, we validate our method through experiments with hardware implementations on FPGAs. The advantages of our method also include the adaptability to be employed to attack other MPUF variations like cMPUF and rMPUF, and the capability to be integrated with an existing attack method for launching efficient attack on MPUFs leveraging reliability information. © 2025 IEEE.

关键词： modeling attack multiplexer-based PUF Physical unclonable function

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A Review on Solar Panel Adoption in Residential Malaysia: Trends, Benefits and Technologies 2nd

A Review on Solar Panel Adoption in Residential Malaysia: Tr...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Quan, Teoh Chee Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Petaling Jaya Selangor Sunway City47500 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Informatics Engineering University of Aleppo Aleppo Syria School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This survey provides detailed perspectives on Malaysian solar energy system adoption in relation to the economic outcome and environmental and policy context. This work has been done by using comprehensive keyword lists and inclusion criteria to confirm that the findings of this survey capture as many relevant publications as possible from the research period from 2010 to 2024. Unlike numerous papers that investigate such systems with mere commercial or industrial perspective, this survey aims at assessing residential installation, and providing accurate cost benefit analysis while showing the business belief of such schemes. It also examines various opinions, especially the Malaysian government policies and strategic incentives and staking its success on public awareness and acceptance. We equally allocate a considerable portion of this survey to discussing the environmental effects and the life cycle analyses comparing solar energy with other energy types. Notably, the survey is also under sustainable development goal 7 with the goal of achieving universal access to affordable reliable, sustainable and modern energy. Such a focus also makes the identified topics relevant for Malaysia, which is useful for references in subsequent projects and activities. These would include use of web-survey, the coverage on the residential sector, the ability to incorporate policy analysis and emphasis on sustainability to the study on solar energy usage in residential Malaysia. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clean energy

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Intelligent Guitar Chord Recognition Using Spectrogram-Based Feature Extraction and AlexNet Architecture for Categorization

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 758-767页

作者： Korade, Nilesh B. Salunke, Mahendra B. Bhosle, Amol A. Sangve, Sunil M. Joshi, Dhanashri M. Asalkar, Gayatri G. Kadu, Sujata R. Sarwade, Jayesh M. Department of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India Department of Computer Engineering PCET’s Pimpri Chinchwad College of Engineering and Research Ravet Maharashtra Pune412101 India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Bibwewadi Pune411037 India Department of Computer Engineering Terna Engineering College Maharashtra Mumbai India Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Chord prediction plays a key role in the advancement of musical technological innovations, such as automatic music transcription, real-time music tutoring, and intelligent composition tools. Accurate chord prediction can assist musicians, educators, and developers in constructing tools that help in learning, playing, and composing music. Background noise and audio distortions may have an impact on chord prediction accuracy, particularly in real-world situations. Chords can have distinct voicings or finger positions on the guitar, resulting in slight variations in audio representation. This study focuses on the classification of guitar chords using techniques of deep learning. There are eight major and minor guitar chords in the dataset. They have been turned into spectrograms, chromagrams, and Mel Frequency Cepstral Coefficients (MFCC) so that features can be extracted. Various deep learning architectures, including CNN, ResNet50, AlexNet, and VGG, were employed to classify the chords. Experimental results demonstrated that the spectrogram-based AlexNet model outperforms others, achieving good accuracy and robustness in chord classification. The proposed study demonstrates the efficiency of spectrograms and advanced deep learning models for audio signal processing in music applications. By automating chord detection, this study provides beneficial resources for music learners as well as educators, enabling more efficient learning and real-time feedback during practice sessions. © (2025), (Science and Information Organization). All Rights Reserved.

关键词： Deep learning

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Enhancing Multi-Source Ambient Backscatter Communication with Transmit Power Coordination

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IEEE Communications Letters 2025年

作者： Cheng, Diancheng Wu, Fan Zhang, Cong Niyato, Dusit Liu, Yuan'an Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing100876 China College of Computing and Data Science Nanyang Technological University 639798 Singapore

In practical massive Internet of Things (IoT), the backscatter device (BD) in ambient backscatter communication (AmBC) systems can be illuminated by multiple ambient sources. However, multiple sources cause severe multi-source direct-link interference (M-DLI) to the BDs while providing multi-source backscattering. The signal-to-noise-plus-interference ratio (SINR) is sensitive to the transmit power of the sources. To fully benefit from multiple sources, this letter introduces transmit power coordination (TPC) strategies to suppress M-DLI and enhance backscattering by tuning the transmit power of the sources within their available ranges, which is particularly suitable for densely deployed passive BDs. To conduct the TPC strategies, the primary objective is to minimize the bit-error-rate (BER) in the multi-source AmBC system by solving a Rayleigh quotient (RQ) maximization problem. Simulation results show that the proposed TPC-assisted receive beamforming method achieves more efficient symbol detection than traditional AmBC detectors and outperforms conventional multi-antenna techniques, especially under low direct-link signal-to-noise ratio (SNR) conditions. © 1997-2012 IEEE.

关键词： Signaling

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SRv6-INT Enabled Network Monitoring and Measurement: Toward High-Yield Network Observability for Digital Twin 3rd

SRv6-INT Enabled Network Monitoring and Measurement: Toward...

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3rd International Conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Zhang, Xu Feng, Chuan Han, Pengchao Liu, Wei Gong, Xiaoxue Zhang, Qiupei Guo, Lei School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9789819616978

Network monitoring and measurement is an important part of realizing the network digital twin. However, it introduces the problem of high cost when obtaining the status data of physical networks. Therefore, to efficiently achieve network observability, we first introduce potential techniques and network scenarios for implementing network measurements. Then, a High-Yield Segment Routing over IPv6 based In-band Network Telemetry (HY-SRv6-INT) system is proposed in this paper. In particular, we design the HY-SRv6-INT system architecture. It consists of IPv6 switches implemented by the P4 language and a Software-Defined Networking (SDN) controller containing the HY-SRv6-INT application. Next, we experimentally demonstrate a packet-level path-associated network measurement process. In addition, the performance of the HY-SRv6-INT system is also quantitatively evaluated to verify its advantages. Finally, we discuss some research challenges and open issues for future network telemetry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Internet protocols

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An Efficient Algorithm for Software Reliability Prediction via Harris Hawks Optimization 2nd

An Efficient Algorithm for Software Reliability Prediction v...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Kong, Li Sheng Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Informatics Engineering University of Aleppo Aleppo Syria School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Software reliability has become an increasingly important characteristic as software systems continue to be integrated into all parts of our lives. While the occurrence of failure after a period of time is within the nature of all software systems, the ability to estimate and predict failure is greatly beneficial to software development and maintenance. Hence, Software Reliability Growth Models (SRGMs) were developed to fulfill this requirement. Conventional parameter estimation methods of SRGM functions were complex and left more to be desired. Consequently, metaheuristics such as Swarm Intelligence algorithms were introduced to estimate and optimize the parameters of SRGM functions. Likewise, Swarm Intelligence algorithms were found to be exceptionally effective in estimating and optimizing the parameters of SRGM functions. This paper presents a novel approach to parameter estimation on three different SRGM functions through Harris’ Hawks Optimization (HHO). The proposed HHO design is compared against numerous different variants of Swarm Intelligence algorithms and is shown to be significantly more efficient than the majority of the compared designs. Moreover, as the proposed design is a base variant of Swarm Intelligence algorithms, various avenues of future improvements have also been presented to improve its overall prediction accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Harris’ Hawks Optimization Parameter Estimation Software Reliability Growth Models Software Reliability Prediction Swarm Intelligence

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Machine learning for Internet of Things (IoT) device identification: a comparative study

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PeerJ Computer Science 2025年 11卷 1-34页

作者： Tahaei, Hamid Liu, Anqi Forooghikian, Hamid Gheisari, Mehdi Zaki, Faiz Anuar, Nor Badrul Fang, Zhaoxi Huang, Longjun Institute of Artificial Intelligence Shaoxing University Shaoxing China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Department of Management Islamic Azad University of Kish Kish Iran Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Selangor Kuala Lumpur Malaysia Department of Computer Science and Engineering Shaoxing University Shaoxing China

The rapid deployment of millions of connected devices brings significant security challenges to the Internet of Things (IoT). IoT devices are typically resource-constrained and designed for specific tasks, from which new security challenges are introduced. As such, IoT device identification has garnered substantial attention and is regarded as an initial layer of cybersecurity. One of the major steps in distinguishing IoT devices involves leveraging machine learning (ML) techniques on device network flows known as device fingerprinting. Numerous studies have proposed various solutions that incorporate ML and feature selection (FS) algorithms with different degrees of accuracy. Yet, the domain needs a comparative analysis of the accuracy of different classifiers and FS algorithms to comprehend their true capabilities in various datasets. This article provides a comprehensive performance evaluation of several reputable classifiers being used in the literature. The study evaluates the efficacy of filter-and wrapper-based FS methods across various ML classifiers. Additionally, we implemented a Binary Green Wolf Optimizer (BGWO) and compared its performance with that of traditional ML classifiers to assess the potential of this binary meta-heuristic algorithm. To ensure the robustness of our findings, we evaluated the effectiveness of each classifier and FS method using two widely utilized datasets. Our experiments demonstrated that BGWO effectively reduced the feature set by 85.11% and 73.33% for datasets 1 and 2, respectively, while achieving classification accuracies of 98.51% and 99.8%, respectively. The findings of this study highlight the strong capabilities of BGWO in reducing both the feature dimensionality and accuracy gained through classification. Furthermore, it demonstrates the effectiveness of wrapper methods in the reduction of feature sets. 2025 Tahaei et al.

关键词： Cyber attacks

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Graph-Based Cooperation Multi-Agent Reinforcement Learning for Intelligent Traffic Signal Control

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IEEE Internet of Things Journal 2025年第10期12卷 14362-14374页

作者： Shang, Jing Meng, Shunmei Hou, Jun Zhao, Xiaoran Zhou, Xiaokang Jiang, Rong Qi, Lianyong Li, Qianmu Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210049 China Nanjing Vocational University of Industry Technology School of Social Science Nanjing210046 China Qufu Normal University School of Computer Science Qufu273165 China Kansai University Faculty of Business and Data Science Osaka Prefecture 564-8680 Japan Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650399 China College of Computer Science and Technology Qingdao266580 China

In the trend of continuously advancing urban intelligent transport construction, traditional traffic signal control (TSC) struggles to make effective decisions with complex traffic conditions. Although multi-agent deep reinforcement learning (MARL) shows promise in optimizing traffic flow, most existing studies ignore the complex relationships between signal lights and fail to communicate with neighbors effectively. Moreover, the deterministic strategies generated by Q-learning-based methods struggle to be extended to large-scale urban road networks. Therefore, this paper proposes a multi-agent graph-based soft actor-critic (MAGSAC) approach for TSC, which combines graph neural networks with the Soft Actor-Critic (SAC) algorithm and extends it to multi-agent environments to address the TSC problem. Specifically, we employ graph-based networks and attention mechanism to expand the receptive domain of agents, enable environmental information to be shared among agents, and utilize the attention mechanism to filter out unimportant information. The algorithm adheres to the Centralized Training Decentralized Execution (CTDE) paradigm to minimize the non-stationarity of MARL. Finally, a rigorous experimental evaluation was conducted using the CityFlow simulator on both synthetic traffic grids and real-world urban road networks. Experimental results show that MAGSAC outperforms other TSC methods in performance metrics, including average queue length and waiting time, and achieves excellent performance under complex urban traffic conditions. © 2025 IEEE.

关键词： Deep reinforcement learning

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Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation 22

Securing Consumer IoT Swarms Using Graph Transformers and SR...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Kohli, Varun Kohli, Bhavya Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln United States

ISBN: (纸本)9798331508050

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a ∼ 10^-4 second inference latency on a laptop CPU. © 2025 IEEE.

关键词： Anomaly Detection Firmware Attestation Graph Neural Network (GNN) Internet of Things (IoT) Malware Detection Security Swarm Attestation

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