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Enhancing Federated Feature Selection through Synthetic Data and Zero Trust Integration

IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2126-2140页

作者： Madathil, Nisha Thorakkattu Alrabaee, Saed Belkacem, Abdelkader Nasreddine United Arab Emirates University College of IT Department of Information Systems and Security United Arab Emirates United Arab Emirates University College of IT Information Systems and Security department United Arab Emirates UAE University Computer and Network Engineering Department Al Ain United Arab Emirates

Federated Learning (FL) allows healthcare organizations to train models using diverse datasets while maintaining patient confidentiality collaboratively. While promising, FL faces challenges in optimizing model accuracy and communication efficiency. To address these, we propose an algorithm that combines feature selection with synthetic data generation, specifically targeting medical datasets. Our method eliminates irrelevant local features, identifies globally relevant ones, and uses synthetic data to initialize model parameters, improving convergence. It also employs a zero-trust model, ensuring that data remain on local devices and only learned weights are shared with the central server, enhancing security. The algorithm improves accuracy and computational efficiency, achieving communication efficiency gains of 4 to 14 through backward elimination and threshold variation techniques. Tested on a federated diabetic dataset, the approach demonstrates significant improvements in the performance and trustworthiness of FL systems for medical applications. © 1983-2012 IEEE.

关键词： Federated learning

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IoT-Based Smart Beehive Monitoring System 6

IoT-Based Smart Beehive Monitoring System

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Liyanage, Nisal Attanayaka, Chamindra Perera, Tarinya Neilkumara, Daniru Bandara, Isuru Sri Chandrasiri, Lakshman NSBM Green University Town Department of Computer Science and Data Science Homagama Sri Lanka NSBM Green University Town Department of Computer Security and Network Systems Homagama Sri Lanka NSBM Green University Town Department of Electrical Electronic and Systems Engineering Homagama Sri Lanka

ISBN: (纸本)9798331517878

In recent years, there has been an alarming decline in bee populations both globally and in Sri Lanka, severely impacting ecosystems and agriculture worldwide. Traditional methods in Sri Lankan beekeeping are often time-consuming and less efficient, making hive management and health assessment challenging. This paper introduces a smart IoT-based beehive monitoring system tailored to the Sri Lankan beekeeping industry. The system integrates various sensors to measure key hive parameters, such as temperature, humidity, and weight, offering beekeepers a user-friendly web interface for real-time monitoring of hive conditions. It also provides insights into hive management aspects, including feeding needs, optimal harvesting times, and abnormal temperature detection within the hive. This paper highlights the initial steps towards implementing a robust IoT-based beehive monitoring system, acknowledging its current limitations. By addressing these limitations in the future, the authors aim to develop a more effective and efficient monitoring method for Sri Lankan beekeepers, saving time and enhancing the productivity and health of bee colonies. © 2024 IEEE.

关键词： Electronic health record

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Comparative Analysis between Supervised and Anomaly Detectors Against Electricity Theft Zero-Day Attacks

Comparative Analysis between Supervised and Anomaly Detector...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Badr, Mahmoud M. Baza, Mohamed Rasheed, Amar Kholidy, Hisham Abdelfattah, Sherif Zaman, Tarannum Shaila SUNY Polytechnic Institute Department of Network and Computer Security UticaNY United States College of Charleston Department of Computer Science CharlestonSC United States Sam Houston State University Department of Computer Science HuntsvilleTX United States Bradley University Department of Computer Science and Information Systems IL United States SUNY Polytechnic Institute Department of Computer and Information Science UticaNY United States

ISBN: (纸本)9798350351408

Smart power grids are vulnerable to electricity theft cyber-attacks, where malicious consumers hack their smart meters (SMs) to down-scale electricity usage readings before reporting them to electric utility companies (EUCs). This serious problem causes billions of dollars in losses to the EUCs worldwide and threatens the power grid's stability. Several machine learning (ML)-based solutions have been advised in the literature for electricity theft detection. However, most existing works propose supervised detection approaches and only a few works propose anomaly detection approaches. Therefore, in this paper, we investigate the effectiveness of the two approaches in detecting electricity theft utilizing a dataset of real electricity usage readings. Specifically, to the best of our knowledge, this work represents the first attempt to implement a comparative analysis between the performance of supervised deep learning (DL) models and anomaly detection models against electricity theft zero-day cyber-attacks. Our experimental results indicate that while the supervised detectors have proven successful against known attacks, they fail to detect new attacks. Moreover, our results demonstrate the superior performance of the anomaly detectors compared to the supervised detectors in defending against electricity theft zero-day attacks. © 2024 IEEE.

关键词： Cyber attacks

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An Android Malware Detection Approach Based on Static Feature Analysis Using Machine Learning Algorithms

An Android Malware Detection Approach Based on Static Featur...

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13th International Conference on Ambient systems, networks and Technologies, ANT 2022 / 5th International Conference on Emerging Data and Industry 4.0, EDI40 2022

作者： Shatnawi, Ahmed S. Yassen, Qussai Yateem, Abdulrahman Department of Computer Information Systems Jordan University of since and Technology Irbid Jordan Department of Network and Security Engineering Jordan University of since and Technology Irbid Jordan

In the past decade, mobile devices became necessary for modern civilization and contributed directly to its development stages in defining mobile information access. Nonetheless, along with these rapid developments in modern mobile devices, security issues rise dramatically, and malware is the most concerning of all. Therefore, many studies and research are still trending in this spectrum, using Machine Learning approaches to prevent and reduce malware's impact. This paper seeks to add to what is already a foundation of various malware detection efforts by presenting a static base classification approach for malware detection based on android permissions and API calls. This approach is based on three well-known Machine Learning algorithms, Support Vector Machines (SVM), K-nearest neighbors (KNN), and Naive Bayes (NB) against a comprehensive new Android malware dataset (CICInvesAndMal2019), in pursuit of achieving high malware detection rates and contribution to the efforts and studies in protecting the development of mobile information. access. © 2022 Elsevier B.V.. All rights reserved.

关键词： Android Malware CICInves KNN Machine Learning Mal2019 Dataset Malware Detection Mobile Information Access Naive Bayes SVM

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Hospital Scheduling systems Current Challenges and Emerging Solutions

Hospital Scheduling Systems Current Challenges and Emerging ...

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2024 Global Digital Health Knowledge Exchange and Empowerment Conference, gDigiHealth.KEE 2024

作者： Musa, Nura Shifa Rafique, Saida Hafsa Mathew, Elezabeth College of Engineering Al Ain University Al Ain United Arab Emirates United Arab Emirates University Department of Information Systems and Security Al Ain United Arab Emirates United Arab Emirates University Department of Computer and Network Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798331531331

Hospital scheduling systems are integral components of healthcare management, addressing the complex challenge of optimizing appointment schedules. In the realm of healthcare research, the Appointment Scheduling Problem (ASP) has garnered significant attention due to its multifaceted nature. This problem is particularly pronounced in the outpatient setting, where effective scheduling systems can significantly impact patient satisfaction and overall health care efficiency. Recent comprehensive reviews highlighted the complexity of the Appointment Scheduling Problem within healthcare services, emphasizing the need for innovative solutions and proposing new scheduling models. This paper reviews literature on the topic of hospital scheduling systems, then looks into the current challenges prevalent in it and the emerging solutions that are discussed in literature. The review offers opportunities for innovation and improvements in scheduling procedures while highlighting difficulties with allocation of resources, appointment scheduling, and technology integration. Using advanced technologies into healthcare such as blockchain and AI-driven algorithms, are said to be integral to move in the direction of care that is patient-centric and efficient operations. The study highlights limitations in hospital scheduling systems, including scalability and generalizability, operational uncertainties, need for real-world implementation, and lack of consideration for emergency cases. To improve patient outcomes and the efficiency of healthcare scheduling, ongoing research and development, stakeholder involvement, user-centred design, and longitudinal research are essential. In summary, hospital scheduling systems represent a dynamic area of research, addressing the intricate interplay between patient satisfaction, resource optimization, and technological advancements. © 2024 IEEE.

关键词： User centered design

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An Efficient Blockchain-Based Healthcare System Using Artificial Intelligence

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computers, Materials & Continua 2022年第5期71卷 2721-2738页

作者： Aitizaz Ali Muhammad Fermi Pasha Ong Huey Fang Jehad Ali Mohammed A.Al.Zain Mehedi Masud Department of Software Systems and Cyber-Security School of ITMonash University47500Subang JayaMalaysia Department of Computer Engineering and Department of AI Convergence Network Ajou UniversitySuwon16499Korea Department of Information Technology College of Computers and Information TechnologyTaif University11099Taif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif University11099Taif21944Saudi Arabia

Personal health records and electronic health records are considered as the most sensitive information in the healthcare *** solutions have been provided for implementing the digital health system using blockchain,but there are several challenges,such as secure access control and privacy is one of the prominent ***,we propose a novel framework and implemented an attribute-based access control system using ***,we have also integrated artificial intelligence(AI)based approach to identify the behavior and activity for security *** current methods only focus on the related clinical records received from a medical ***,existing methods are too inflexible to resourcefully sustenance metadata changes.A secure patient data access framework is proposed in this research,integrating blockchain,trust chain,and blockchain methods to overcome these problems in the literature for sharing and accessing digital healthcare *** have used a neural network and classifier to categorize the user access to our proposed *** proposed scheme provides an intelligent and secure blockchain-based access control system in the digital healthcare *** results surpass the existing solutions by collecting attributes such as the number of transactions,number of nodes,transaction delay,block creation,and signature verification time.

关键词： Personal health records blockchain technology artificial intelligence access control attributes health care system

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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

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Engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system network science Overexploitation Regime shift Metastability

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Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions

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Computational and Structural Biotechnology Journal 2025年 28卷 217-238页

作者： Nisha Thorakkattu Madathil Fida K. Dankar Marton Gergely Abdelkader Nasreddine Belkacem Saed Alrabaee Department of Information Systems and Security United Arab Emirates University 15551 Al Ain United Arab Emirates CHEO Research Institute Ottawa Canada Department of Computer and Network Engineering United Arab Emirates University 15551 Al Ain United Arab Emirates

Federated learning (FL)–a distributed machine learning that offers collaborative training of global models across multiple clients. FL has been considered for the design and development of many FL systems in various domains. Hence, we present a comprehensive survey and analysis of existing FL systems, drawing insights from more than 250 articles published in 2019-2024. Our review elucidates the functioning of FL systems, particularly in comparison with alternative distributed learning approaches. Considering the healthcare domain as an example, we define the building blocks of a typical FL healthcare system, including system architecture, federation scale, data partitioning, open-source frameworks, ML models, and aggregation algorithms. Furthermore, we identify and discuss key challenges associated with the design and implementation of FL systems within the healthcare sector while outlining the directions of future research. In general, through systematic categorization and analysis of existing FL systems, we offer insights to design efficient, accurate, and privacy-preserving healthcare applications using cutting-edge FL techniques.

关键词： Federated learning Aggregation algorithms Data privacy Data partitioning Non-identically distributed data Attacks

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Defensive Auto-Updatable and Adaptable Bot Recommender System (DAABRS): A New Architecture Approach in Cloud Computing systems 6

Defensive Auto-Updatable and Adaptable Bot Recommender Syste...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Khwaldeh, Sufian Mohit, Y. Khushwant, S. Department of Network and Information Security College of lnformation Technology University of Fujairah 1207 United Arab Emirates Department of Information Technology Faculty of Information Technology and Systems The University of Jordan Aqaba77110 Jordan Department of Mathematics University Institute of Sciences Chandigarh University Punjab Mohali140413 India Department of Computer Science & Engineering University Institute of Engineering and Technology M. D. University Rohtak124001 India

ISBN: (纸本)9798350394634

Cloud server data security makes extensive use of data centers and instantly accessible edge systems for possible transactions involving sizable and confidential data. These Edge systems are chosen for rental based on their usefulness in situations where new privacy and security difficulties must be overcome. The use of Edge systems is subject to dynamic mobility, which increases the danger of attacking threats. With this suggested approach, the Defensive Auto-updatable and Adaptable Bot Recommender approach (DAABRS) is used by the Cloud Center to identify the safeguarded Edge System. Cloud Data Centers with Edge systems contain a variety of users and environments, making it simple for attackers to enter anonymously and compromise Edge System privacy. The most effective security measure against increasing risks and assaults for Edge systems that rent more cloud storage is to use Stealth Mode Bots inside of data segments. In addition to anticipating and averting data breaches in server partitioning, tenant systems, edge systems, and containers by integrating stealth mode bots into each. Suggested use cases: Tenants, Edge systems, or Containers for effective security-enhanced Stealth Mode Bots that are activated when a particular data segment is unplugged. Updatable and adaptive bots are used in the suggested strategy to counter Edge strategy assaults. Additionally, it uses Stealth Mode Bots to stop heavily attacked Data Containers, and machine learning will be used to update specifications to identify and prevent further assaults. © 2024 IEEE.

关键词： Containers

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Deep Learning Convolutional Neural networks Classification Based on Brain Cancer MRI

Deep Learning Convolutional Neural Networks Classification B...

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2023 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Zeebaree, Diyar Qader Asaad Zebari, Dilovan Zeebaree, Imad Majed Abduljabbar Mohammed, Mohammed Mohammed, Bafreen Abduljabbar Asaad Zebari, Nechirvan Duhok Polytechnic University Technical College of Informatic Department of Computer Network and Information Security Kurdistan Region Duhok42001 Iraq Nawroz University College of Science Department of Computer Science Kurdistan Region Duhok42001 Iraq Duhok Private Technical Institute Department of Health Management Information Systems Kurdistan Region Duhok42001 Iraq University of Duhok College of Education Department of Mathematic Kurdistan Region Duhok42001 Iraq

ISBN: (纸本)9798350322972

Brain tumor diagnosis and classification is a major challenge for medical field. The majority of the time, computer-Aided Diagnostic systems (CADS) and methods involving Deep learning (DL) are utilized to find brain tumors quickly and precisely. Artificial diagnostic systems, on the other hand, are not very accurate, the physician is not using them very well when diagnosing Magnetic Resonance Images (MRI). By applying image processing principles, it is possible to conceptualize the different human tissue structures. It is difficult to detect diseased structures in the human brain using only the most fundamental imaging methods. In this research, we present a model-based Convolutional Neural network (CNN) structure for identifying and classifying brain tumors. The proposed method consists of two parts: pre-processing and classification. In this study, CNN architecture outperformed in state-of-the-art methods, with 99.87% accuracy. F1-score 99.88%, 99.76% precision. © 2023 IEEE.

关键词： Magnetic resonance

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