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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Srimathi. J K. Kanagasabapathi Kirti Mahajan Shahanawaj Ahamad E. Soumya Shivangi Barthwal Department of Information Technology KPR College Of Arts Science And Research Arasur Covai Computer Science and Technology Karpagam College of Engineering Coimbatore Department of Information Technology Institute of Management & Entrepreneurship Development Bharati Vidyapeeth Deemed University Pune Department of Software Engineering College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Department of Computer Science and Engineering St. Martins Engineering College Secunderabad Telangana Department of Electronics & Communication Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

The complex landscape of multi-cloud settings is the result of the fast growth of cloud computing and the ever-changing needs of contemporary organizations. Strong cyber defenses are of fundamental importance in this setting. In this study, we investigate the use of AI in hybrid cloud settings for the purpose of multi-cloud security management. To help businesses improve their productivity and resilience, we provide a mathematical model for optimal resource allocation. Our methodology streamlines dynamic threat assessments, making it easier for security teams to efficiently priorities vulnerabilities. The advent of a new age of real-time threat response is heralded by the incorporation of AI-driven security tactics. The technique we use has real-world implications that may help businesses stay ahead of constantly changing threats. In the future, scientists will focus on autonomous security systems, interoperability, ethics, interoperability, and cutting-edge AI models that have been validated in the real world. This study provides a detailed road map for businesses to follow as they navigate the complex cybersecurity landscape of multi-cloud settings, therefore promoting resilience and agility in this era of digital transformation.

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A column generation algorithm with dynamic constraint aggregation for minimum sum-of-squares clustering

arXiv

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arXiv 2024年

作者： Sudoso, Antonio M. Aloise, Daniel Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Department of Computer and Software Engineering Polytechnique Montréal 2500 Chem. de Polytechnique MontréalH3T 1J4 Canada

The minimum sum-of-squares clustering problem (MSSC), also known as k-means clustering, refers to the problem of partitioning n data points into k clusters, with the objective of minimizing the total sum of squared Euclidean distances between each point and the center of its assigned cluster. We propose an efficient algorithm for solving large-scale MSSC instances, which combines column generation (CG) with dynamic constraint aggregation (DCA) to effectively reduce the number of constraints considered in the CG master problem. DCA was originally conceived to reduce degeneracy in set partitioning problems by utilizing an aggregated restricted master problem obtained from a partition of the set partitioning constraints into disjoint clusters. In this work, we explore the use of DCA within a CG algorithm for MSSC exact solution. Our method is fine-tuned by a series of ablation studies on DCA design choices, and is demonstrated to significantly outperform existing state-of-the-art exact approaches available in the literature. Copyright © 2024, The Authors. All rights reserved.

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Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Xiyue Xue, Xiaoyong Du, Xiaoning Xie, Xiaofei Liu, Yang Sun, Meng University of Oxford Department of Computer Science United Kingdom Peking University Department of Information and Computing Science School of Mathematical Science China Monash University Department of Software Systems and Cybersecurity Australia Singapore Management University School of Computing and Information Systems Singapore Nanyang Technological University School of Computer Science and Engineering Singapore

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify dissimilarities between clients, identifying model deviations caused by backdoor injections. We also propose an iterative algorithm to progressively detect and exclude malicious clients as outliers based on these dissimilarity measurements. Evaluations across a range of benchmark tasks demonstrate that our approach outperforms state-of-the-art methods in detection accuracy and defense effectiveness. When deployed for runtime protection, our approach effectively eliminates backdoor injections with marginal overheads. © 2025 IEEE.

关键词： Federated learning

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Hybrid Makes Better: Privacy-Preserving Medical Image Classification Based on Federated Learning

Hybrid Makes Better: Privacy-Preserving Medical Image Classi...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Zhi Wang Haotian Chi Yonggang Li Yuxuan Zhang Shunrong Jiang Xiaojiang Du Mohsen Guizani School of Computer Science China University of Mining and Technology Xuzhou China School of Automation and Software Engineering Shanxi University Taiyuan China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Machine Learning Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The power of machine learning makes it available for medical image classification. However, machine learning requires large medical datasets to improve accuracy, which will involve patients’ private information and lead to their privacy leakage. Federated learning is a trending technique to both protect patients’ privacy and improve the accuracy of medical image classification. Unfortunately, current research shows that federated learning faces the risk of privacy leakage. In this paper, we propose a privacy-preserving scheme FHDM. Specifically, we construct a local hybrid differential privacy algorithm (LHDP) against patients’ privacy leakage. This hybrid algorithm utilizes both Gaussian and Laplace differential privacy without enlarging the privacy budget. We prove that the algorithm applies to the model parameter. Moreover, we design loss optimization and global optimization strategies on the algorithm to achieve higher accuracy in medical image classification. Finally, we validate FHDM in terms of privacy-preserving and model accuracy on real datasets. Experiments show that FHDM effectively protects privacy and improves the average accuracy by 10.0% compared to adopting the LDP-based scheme with the same medical image dataset and privacy budget.

关键词： Privacy Differential privacy Accuracy Federated learning Inference algorithms Classification algorithms Security Optimization Biomedical imaging Image classification

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Enhancing IoT-Based Smart Farming Security with a Hybrid GRU Intrusion Detection System

Enhancing IoT-Based Smart Farming Security with a Hybrid GRU...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Sivakumar Nagarajan S. Vijay S. Karthi RVS Praveen Ramesh Vijaya Babu K Burri Naresh Technical Architect I & I Software Inc Bethlehem USA Department of Data Science Sacred Heart College (Autonomous) Tamilnadu India Department of Computer Science and Engineering V.S.B. Engineering College Karur Tamilnadu India Director Digital Engineering & Assurance LTIMindtree Limited Hyderabad Telangana India SRKR Engineering College Bhimavaram Andhra Pradesh India Department of CSE - Cyber Security CVR College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The growing use of IoT devices in smart agriculture makes the industry vulnerable to several cyber risks. This study introduces an innovative hybrid Intrusion Detection System (IDS) utilising Gated Recurrent Units (GRUs) to improve the security of IoT-enabled smart agriculture. compared to current LSTM and CNN-based intrusion detection systems, the proposed model effectively incorporates sequential data dependencies, hence minimising false positives and enhancing detection accuracy. Experimental findings indicate that the suggested model surpasses conventional techniques, with an accuracy of 97.5%, an F1 score of 0.96, and a recall of 0.97. These developments create a strong, efficient, and scalable solution for safeguarding IoT-based agricultural systems from cyber-attacks.

关键词： Smart agriculture Technological innovation Accuracy Computational modeling Scalability Intrusion detection Internet of Things Object recognition Protection Long short term memory

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Predicting bolt stress under uncertainty in offshore wind turbine structural connections using a gaussian process surrogate model 31st

Predicting bolt stress under uncertainty in offshore wind tu...

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31st International Ocean and Polar engineering Conference, ISOPE 2021

作者： Jorgensen, Jack Hodkiewicz, Melinda Cripps, Edward Hassan, Ghulam Mubashar Department of Mechanical Engineering University of Western Australia Crawley Australia Department of Mathematics and Statistics University of Western Australia Crawley Australia Department of Computer Science and Software Engineering University of Western Australia Crawley Australia

ISBN: (纸本)9781880653821

Fatigue assessment of bolted ring-flanges in offshore wind turbine structures is a critical step in the overall structural design process. Analytical and numerical methods are employed to predict bolt stress, offering the convenience of speed (analytical) or accuracy (numerical). In this paper, we investigate the application of a Gaussian Process (GP) surrogate model, leveraging the accuracy of numerical techniques with significant gains in computation times. We present the notion of predictive uncertainty available from the GP surrogate model. Finally, we demonstrate the capacity of the GP surrogate model to propagate input space uncertainty in a computationally efficient manner. © 2021 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Finite element method

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Renewable Energy-Powered BLDC Motor-Based Appliances Based on Supercapacitor-Assisted Converters for DC Homes

Renewable Energy-Powered BLDC Motor-Based Appliances Based o...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Nirashi Polwaththa Gallage Nihal Kularatna Alistair Steyn-Ross Dulsha Kularatna-Abeywardana School of Engineering University of Waikato Hamilton New Zealand Department of Electrical Computer and Software University of Auckland line 2-name of organization acronyms acceptable Auckland New Zealand

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

Today solar or wind energy resources are coupled with the AC grid supply in the household using inverters and battery banks. However, the maximum power point tracking-based battery chargers and inverters have significant losses in the renewable-to-AC grid link. By using supercapacitor modules with extra low-frequency power converters, we can increase the end-to-end efficiency of whiteware such as refrigerators, washing machines, dryers, and heat pumps. Given that the modern inverter-driven whiteware carries brush-less DC motors driven by an inverter supplied by an internal DC bus derived from the line frequency AC supply, these systems can be easily powered by a direct DC source for the reduction of losses associated with the renewable energy link in a household. In addition, this could also reduce the converter losses within the white-good. The paper presents the theoretical and experimental details on how commercial whiteware can be easily modified to operate from a DC rail with a supercapacitor module used for the dual purpose of power conversion and energy buffering based on an example of a refrigerator.

关键词： Rails Renewable energy sources Wind energy Heat pumps Filtering Supercapacitors Frequency conversion Inverters Washing machines Power conversion

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Dynamic Selection Scheme of LTE-M and 5G for Efficient Maritime Communication Based on Traffic Volume 24

Dynamic Selection Scheme of LTE-M and 5G for Efficient Marit...

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24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Yoon, Mahnsuk Cho, Kyucheol Kwon, Jaeuk Seo, Jihun Lee, Hyunjoo Yun, Jangkyu Gumi Korea Republic of School of Computer Secience and Engineering Software Education Center Kyungpook National University Daegu Korea Republic of Korea Transport Institute Center for Connected & Automated Driving Research Department of The Fourth Industrial Revolution & Transport Sejong-si Korea Republic of Gumi Korea Republic of

ISBN: (纸本)9788995004395

This paper investigates the technology for selecting communication scheme based on the amount of traffic in LTE-M (Marine) and 5G communication environments. Recently, it has been possible to use LTE-M and 5G communications for preventing collisions between ships and obstacles in coastal areas, moving from a focus on terrestrial users. This paper proposes a scheme for efficiently handling both small-scale and large-volume traffic generated from terminals on ships. LTE-M can accommodate more terminals by utilizing the wide coverage of coastal areas, but it can be vulnerable to high-volume video processing for real-time obstacle detection. 5G provides a high data transmission rate and low latency, making it suitable for large data exchanges between ships or real-time services, but its short coverage makes it unsuitable for accommodating a wide area. Therefore, this paper proposes a scheme for dynamically selecting LTE-M or 5G communication schemes according to the amount of data generated by terminals on ships, to transmit data. In this paper, we propose an algorithm to decide which communication scheme, LTE-M or 5G, should be chosen based on the amount and type of traffic. The algorithm dynamically selects the optimal communication scheme considering real-time traffic information and network status. By implementing a simulation environment for coastal areas using the technique proposed in this paper, we compare the scheme where terminals installed on ships selectively use LTE-M and 5G communication schemes with the scheme where two communication schemes are chosen randomly for transmission. The simulation results confirm that the proposed technique can process more data compared to the existing schemes. Copyright 2023 KICS.

关键词： Ships

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Combinatorial optimization and reasoning with graph neural networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 5922-5982页

作者： Quentin Cappart Didier Chételat Elias B. Khalil Andrea Lodi Christopher Morris Petar Veličković Department of Computer Engineering and Software Engineering Polytechnique Montréal Montréal Canada CERC in Data Science for Real-Time Decision-Making Polytechnique Montréal Montréal Canada Department of Mechanical & Industrial Engineering University of Toronto Toronto Canada Jacobs Technion-Cornell Institute Cornell Tech and Technion - IIT New York Department of Computer Science RWTH Aachen University Aachen Germany DeepMind London UK

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either directly as solvers or by enhancing exact solvers. The inductive bias of GNNs effectively encodes combinatorial and relational input due to their invariance to permutations and awareness of input sparsity. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at optimization and machine learning researchers.

关键词： combinatorial optimization graph neural networks reasoning

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How to Combine Models? Principles and Mechanisms to Aggregate Fuzzy Cognitive Maps 23

How to Combine Models? Principles and Mechanisms to Aggregat...

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Proceedings of the Winter Simulation Conference

作者： Ryan Schuerkamp Philippe J. Giabbanelli Umberto Grandi Sylvie Doutre Department of Computer Science & Software Engineering Miami University Oxford USA Institut de Recherche en Informatique de Toulouse (IRIT) Université Toulouse Capitole Toulouse FRANCE

ISBN: (纸本)9798350369663

Fuzzy Cognitive Maps (FCMs) are graph-based simulation models commonly used to model complex systems. They are often built by participants and aggregated to compare the viewpoints of homogenous groups (e.g., anglers and ecologists) and increase the reliability of the FCM. However, the default approach for aggregation may propagate the errors of an individual participant, producing an aggregate FCM whose structure and simulation outcomes do not align with the system of interest. Alternative aggregation methods exist; however, there are no criteria to assess the quality of aggregation methods. We define nine desirable criteria for FCM aggregation algorithms and demonstrate how three existing aggregation procedures from social choice theory can aggregate FCMs and fulfill desirable criteria, enabling the assessment and comparison of FCM aggregation procedures to support modelers in selecting an aggregation algorithm. Moreover, we classify existing aggregation algorithms to provide structure to the growing body of aggregation approaches.

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