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AAMB: a cross-domain identity authentication scheme based on multilayered blockchains in IoMT

Journal of Supercomputing 2025年第7期81卷 1-39页

作者： Zhang, Zheqing Li, Hongzhi Li, Dun Li, Kuan-Ching College of Big Data and Artificial Intelligence Chizhou University Chizhou247000 China Anhui Provincial Joint Construction Key Laboratory of Intelligent Education Equipment and Technology Chizhou247000 China Department of Industrial Engineering Tsinghua University Beijing China Department of Computer Science and Information Engineering Providence University Taichung City Taiwan

The internet of medical things (IoMT) integrates various sensors, smart devices, and fully utilizes medical data to provide high-quality medical services through the Internet of Things (IoT). Smart medical devices can be cross-domain shared in IoMT to provide health monitoring, disease diagnosis, rehabilitation training, etc. In IoMT, conventional authentication schemes struggle to meet cross-domain medical service sharing requirements due to resource constraints (computational/communication), confidentiality challenges, and strict latency demands. To address these issues, we propose a cross-domain authentication scheme based on dynamic accumulators and two-layer blockchains. First, a blockchain-based collaborative authentication framework is designed to realize secure and reliable intradomain and cross-domain authentication. Second, certificateless cryptography and dynamic accumulators are combined to guarantee both the confidentiality and efficiency of cross-domain authentication. Furthermore, we analyze the potential security threats that threaten the system’s stability and formally demonstrate the security of the proposed scheme using Burrows–Abadi–Needham logic. The formal and informal security analyses indicate that the proposed scheme is secure and can resist against known security threats. Finally, we evaluate the performance of the proposed authentication protocols in terms of computational and communication overhead. The corresponding performance analysis shows that compared with related protocols, it can reduce the computational and communication costs by more than 42.3% and 21.4%, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Diagnosis

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ABIDS-VEM: leveraging an equilibrium optimizer and data ramification in association with ensemble learning for anomaly-based intrusion detection system

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Journal of Supercomputing 2025年第7期81卷 1-35页

作者： Verma, Priyanka O’Shea, Donna Newe, Thomas Mehta, Nakul Bharot, Nitesh Breslin, John G. School of Computer Science University of Galway University road GalwayH91TK33 Ireland Department of Computer Science Munster Technological University Rossa Ave Bishopstown CorkT12 P928 Ireland Department of Electronics and Computer Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Data Science Institute University of Galway University road GalwayH91TK33 Ireland Digital Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland

The convergence of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) within the Industry 4.0 paradigm leverages software-defined networking, multi-cloud architectures, and edge/fog computing to enhance industrial processes. However, this digital transformation introduces significant cybersecurity and privacy vulnerabilities within the complex, data-intensive IoT/IIoT ecosystems. To mitigate these risks, this research proposes a novel Anomaly-based Intrusion Detection System using Voting-based Ensemble Model (ABIDS-VEM) in Industry 4.0 environments. The VEM architecture synergistically combines multiple machine learning algorithms and gradient boosting frameworks, including CatBoost (CB), XGBoost (XGB), LightGBM (LGBM), Logistic Regression (LR), and Random Forest (RF), to enhance the precision and computational efficiency of intrusion detection systems (IDS) in IoT/IIoT contexts. The proposed framework incorporates a data ramification process, in which the data is divided into multiple parts, feature selection process which is optimized through the Equilibrium Optimizer (EO) algorithm, and outlier detection utilizing the Isolation Forest (IF) method. Comprehensive empirical evaluations were conducted using three benchmark datasets: XIIoTID, NSL-KDD, and UNSW-NB15, to validate the efficacy of the proposed system. The model achieves high accuracy across datasets: 98.1476% for XIIoT-ID, an impressive accuracy of 98.9671% for NSL-KDD, and 94.1327% for UNSW-NB15 dataset. These experimental results demonstrate the potential of this approach to significantly enhance the resilience of critical industrial systems and data against evolving cyber threats, thereby supporting the continued evolution of Industry 4.0 technologies and bolstering the security posture of IoT/IIoT ecosystems. This research contributes to the ongoing efforts to secure the rapidly expanding digital industrial landscape, offering a robust solution for detecting and mitigating sophistic

关键词： Network intrusion

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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Seer: Accelerating Block chain Transaction Execution by Fine-Grained Branch Prediction 51st

Seer: Accelerating Block chain Transaction Execution by Fine...

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51st International Conference on Very Large data Bases, VLDB 2025

作者： Zhang, Shijie Cheng, Ru Liu, Xinpeng Xiao, Jiang Jin, Hai Li, Bo 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 Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Increasingly popular decentralized applications (dApps) with complex application logic incur significant overhead for executing smart contract transactions, which greatly limits public block chain performance. Pre-executing transactions off the critical path can mitigate substantial I/O and computation costs during execution. However, pre-execution does not yield any state transitions, rendering the system state inconsistent with actual execution. This inconsistency can lead to deviations in pre-execution paths when processing smart contracts with multiple state-related branches, thus diminishing pre-execution effectiveness. In this paper, we develop Seer, a novel public block chain execution engine that incorporates fine-grained branch prediction to fully exploit pre-execution effectiveness. Seerpredicts state-related branches using a two-level prediction approach, reducing inconsistent execution paths more efficiently than executing all possible branches. To enable effective reuse of pre execution results, Seer employs checkpoint-based fast-path execution, enhancing transaction execution for both successful and unsuccessful predictions. Evaluations with realistic block chain workloads demonstrate that Seer delivers an average of 27.7× transaction-level speedup and an overall 20.6× speedup in the execution phase over vanilla Ethereum, outperforming existing block chain execution acceleration solutions. © 2025, VLDB Endowment, All rights reserved.

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TriBoost and Beyond: Advanced Machine Learning Approaches for Diabetes Risk Prediction 3

TriBoost and Beyond: Advanced Machine Learning Approaches fo...

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

作者： Katlariwala, Soham Biren Gandhi, Vaibhav C. Patel, Nirav Parmar, Devendra Desai, Ayushi Indian Institute of Technology Madras B.S. Data Science and Applications Chennai India University Department of Computer Engineering Mbit Anand India Parul University Parul Institute of Technology Vadodara India

ISBN: (纸本)9798331509675

Diabetes is a tremendous world health issue and, in its management, and complications prevention, proper screening is vital. This paper examines the improvement of accurate diabetes risk prediction by some advanced machine learning methods. The study compares various models such as;Random Forest, AdaBoost, Support Vector Machine, CatBoost, Decision Tree, LS-SVM, Logistic Regression, Naïve Bayes, k-Nearest Neighbors, LightGBM, XGBoost, and neural networks. Most emphasis is put on TriBoost that is a novel ensemble model which integrates XGBoost, CatBoost, and LightGBM. Lastly, the effectiveness of a model is determined using measures such as ROC AUC and accuracy. Evaluation of the results has shown that the proposed TriBoost had the highest accuracy at 94% and ROC AUC of 91% thereby outcompeting the use of individual classifiers as well as other ensemble methods. The results of the study confirm adequacy and possibilities of ensemble learning to enhance predictive precision and stability and their applicability for early detection of diabetes in clinics. Further work shall seek to increase the variability of the dataset, integrate fresh clinical data, and increase computational effectiveness for practical use. © 2025 IEEE.

关键词： Adversarial machine learning

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Energy Consumption Optimization using Big data Analytics for Smart Grid Management 5

Energy Consumption Optimization using Big Data Analytics for...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Devi, K. Sumithra Contractor, Dipenkumar Deepa, V. Lakshmi, T.K. Santhikala, M. Mounika, Kamati Kcg College of Technology Department of Computer Science and Engineering Chennai India Government Engineering College Department of Computer Engineering Dahod India Sri Ramakrishna College of Arts and Science Department of Information Technology Tamil Nadu Coimbatore India Malla Reddy University Department of Computer Science and Engineering Telangana Hyderabad India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam India Mb School of Pharmaceutical Sciences Mohan Babu University Department of Pharmacy Practice Andhra Pradesh Tirupathi India

ISBN: (纸本)9798331522100

The application of big data analytics to improve energy usage efficiency in smart grids has been prompted by the rising need for power management. Adding to the above, it poses a big data solution for demand prediction, load consolidation, and fault diagnosis. Establishing the new and state-of-the-art innovative LSTM network that was employed in the forecast yielded a higher demand forecast accuracy of 94.2% compared to the previous systems. The load balancing index achieved 92% with an accuracy of 93.7% in fault detection, thus minimizing maintenance cost by 14.7% while improving grid reliability. The proposed model was found to give better results than previous conventional machine learning techniques and even modern ones which are more complex, especially for an ever-changing grid environment. Overall, these findings show the promise of big data solutions for handling the challenges of the contemporary smart grid while improving its efficiency through energy optimization. Consequently, this work lays a framework for subsequent studies on the remaining applications of advanced analytics in energy management systems. © 2025 IEEE.

关键词： Smart power grids

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Peer-Guided Optimization: Incorporating Collaborative Learning into Stochastic Optimization in Machine Learning

Peer-Guided Optimization: Incorporating Collaborative Learni...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Joy Datta Rawhatur Rabbi Nafiz Imtiaz Rafin Md. Golam Rabiul Alam Department of Computer Science & Engineering School of Data & Sciences Brac University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

A single optimizer is usually used to minimize the cost function in conventional machine learning. This paper introduces Peer-Guided Optimization (PGO), where two optimizers collaborate by sharing gradient information to help each other explore the cost surface and accelerate convergence. Unlike traditional optimization methods, PGO alternates steps between two optimizers across iterations. Based on the proximity to the optimal solution, each optimizer dynamically calculates a guidance parameter that influences the contribution of the peer optimizer during updates. PGO has two variants: Homogeneous PGO (using identical optimizers with different learning rates) and Heterogeneous PGO (combining different types of optimizers, such as Adam and SGD). Experimental results demonstrate that PGO consistently outperforms conventional single-optimizer methods in terms of accuracy and convergence speed, making it a promising strategy for optimizing deep learning models in various applications.

关键词： Electric potential Accuracy Costs Navigation Federated learning Stochastic processes Optimization methods Minimization Optimization Convergence

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Human Evolutionary Optimization Algorithm for Optimal IoT Task Placement in Hybrid Cloud-Fog Networks

Human Evolutionary Optimization Algorithm for Optimal IoT Ta...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Baskar, R. Mohanraj, E. Jaisurya, K. Janani, S. Kesavan, M. K. S. Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode637 215 India K. S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tiruchengode637 215 India K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode637 215 India

ISBN: (纸本)9798331529833

The growing prevalence of Internet of Things (IoT) devices necessitates the strategic allocation of computational activities inside cloud-fog networks to achieve low latency, diminished energy consumption, and optimal resource utilisation. This study introduces a new Human Evolutionary Optimisation Algorithm (HEOA) designed for optimising IoT job placement inside hybrid cloud-fog environments. The suggested method emulates human decision-making to provide adaptive and intelligent job allocation, optimising computational load distribution between centralised cloud data centres and decentralised fog nodes. HEOA employs evolutionary processes, including selection, mutation, and crossover, to dynamically identify the ideal configuration for job offloading, therefore reducing response time and energy expenditures. Simulation studies indicate that HEOA surpasses conventional optimisation methods by attaining quicker convergence, enhanced resource efficiency, and superior scalability. The HEOA diminished energy usage by 12% and cut response time by 15%, rendering it highly compatible with contemporary IoT applications. This study enhances job scheduling frameworks in diverse cloud-fog settings, facilitating improved performance and sustainability in IoT ecosystems. © 2025 IEEE.

关键词： Energy utilization

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Meta Heuristic Image Optimisation in LiDAR Sensor Based on Cloud IoT Network and Deep Learning Algorithms

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 566-575页

作者： Meenakshi, K. Manoj, Gunna Diwakar, Meenakshi Prabakaran, Manjula Sravan, Padala Babu, Gaddam Ravindra Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Cyber Security Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad India Department of IT Panimalar Engineering College Chennai India Dept of CSE (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh Angallu India Computer Science and Engineering NIIT with learning partner Hainan Normal University Haikou China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India

LiDAR technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. The aim of this research is to propose a novel technique in cloud IoT network-based LiDAR sensor imaging with image optimization using a metaheuristic deep learning model. Input has been collected LiDAR image using a cloud IoT module and processed for noise removal and normalization. Then this processed image is classified and optimized utilizing a quantile transfer graph convolutional model with binary firefly whale colony optimization. The experimental analysis is carried out in terms of detection accuracy, random precision, recall, RMSE, and AUC for various LiDAR sensor-based datasets. Comparing the results of the various approaches reveals that the suggested approach outperforms them all with a recall of 94%, a random precision of 95%, and a detection accuracy of 98%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cloud IoT network Convolutional model Image optimization LiDAR sensor Metaheuristic deep learning

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Hybrid Beamforming Optimization in Millimeter Wave MIMO Using Deep Deterministic Policy Gradient Based Deep Reinforcement Learning 3

Hybrid Beamforming Optimization in Millimeter Wave MIMO Usin...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Riadhusin, Rame Sudhakar, K. Prakash, D. Bhanu Kumar, S. Senthil Rajasubha, J. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Cvr College of Engineering Department of Electronics and Communication Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering Technology Department of Computer Science and Engineering Mamallapuram India

ISBN: (纸本)9798331508456

In recent years, the rapid growth of Hybrid Beamforming (HB) technology has developed rapidly to improve high spectrum efficiency and data transmission in wireless communication systems. Massive Multiple Input Multiple Output (MIMO) systems are integrated with HB to encourage wireless network performance by improving signal quality. However, combination of digital and analog beamforming increases hardware complexity and quantization errors in multiple streams. Hence, this research proposes Deep Reinforcement Learning (DRL)-based hybrid beamforming optimization framework to learn relevant Channel State Information (CSI) by learning complex non-linear relationships and beam forming weights dynamically. Initially, Deep Multiple Input Multiple Output (Deep MIMO) dataset which consists of channel measurements and capacity to receive and transmit signal processing is used. After that, preprocessing is performed using Amplitude Scaling (AS) for signal compression and Phase Normalization (PN) to reduce phase noise in multiple frequencies. Finally, Deep Deterministic Policy Gradient (DDPG)-based DRL model is used to perform HB optimization through adaptive learning policy by improving sum rate and spectral efficiency while reducing quantization errors. Proposed method results with the Sum-rate (5.35 bps/HZ), Accuracy (99.26%) and Inference time (0.03 sec) when compared with existing CNN-Bi-directional Long Short-Term Memory (CNNBiLSTM). © 2025 IEEE.

关键词： Beamforming

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