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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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TELEX: Two-Level Learned Index for Rich Queries on Enclave-based Blockchain Systems

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4299-4313页

作者： Wu, Haotian Tang, Yuzhe Shen, Zhaoyan Tao, Jun Lin, Chenhao Peng, Zhe The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Syracuse University Department of Electrical Engineering and Computer Science United States Shandong University School of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Xi'an Jiaotong University Faculty of Electronic and Information Engineering China The Hong Kong Polytechnic University Shenzhen Research Institute Hong Kong

Blockchain has become a popular paradigm for secure and immutable data storage. Despite its numerous applications across various fields, concerns regarding the user privacy and result integrity during data queries persist. Additionally, the need for rich query functionalities to harness the full potential of blockchain data remains an area ripe for exploration. In order to address these challenges, our paper first utilizes a framework based on the Trusted Execution Environment (TEE) and oblivious RAM technique to achieve both privacy and data integrity. To enhance the query efficiency over the entire blockchain, we then devise a two-level learned indexing methodology named TELEX within the TEE for both integer and string keys. We also propose different query processing algorithms for versatile query types, including exact queries, aggregate queries, Boolean queries, and range queries. By implementing the prototype and conducting extensive evaluation, we demonstrate the feasibility and remarkable improvement in efficiency compared to existing solutions. © 1989-2012 IEEE.

关键词： Query languages

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Machine Learning-Enhanced Analysis of Genomic Data for Precision Medicine

Machine Learning-Enhanced Analysis of Genomic Data for Preci...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Sasikala, R. Deepthi, K. Jaya Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy620009 India Mohan Babu University Department of Aiml Ap Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350373783

Precision medicine employs genetic data, which is enhanced with machine learning algorithms, to determine the most relevant treatment method per patient's genotype. We have zoned in on the area of a machine learning-guided genetic information analysis in this study to assist in clinical decisionmaking of precision medicine while improvising. Genetic information for 500 cancer patients cases taken from the public database and cleaned to lean out the desired features were furnished. Different machine learning algorithms were trained, including SVM, random forest, gradient boost, deep learning (CNN) and ensemble (ensemble method). Their performance was investigated for predicting cancer subtypes. The ensemble model reached the top accuracy level (88%), the best sensitivity (85%), ever specificity (91%) and AUC score (0.95). By comparison a group had better result than an individual algorithm could across all metrics. In further, deep learning methods were reported to attain more accuracy and F1 scores which is more than traditional machine learning methods. These findings hence prequalify the usefulness of ML in investigating genotype data in precision medicine, and enable physicians with useful information to select treatment options, make prognosis, and indicate the medical care. © 2024 IEEE.

关键词： Adversarial machine learning

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DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices 38

DapperFL: Domain Adaptive Federated Learning with Model Fusi...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jia, Yongzhe Zhang, Xuyun Hu, Hongsheng Choo, Kim-Kwang Raymond Qi, Lianyong Xu, Xiaolong Beheshti, Amin Dou, Wanchun State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China School of Computing Macquarie University Australia School of Information and Physical Sciences University of Newcastle Australia Department of Information Systems and Cyber Security University of Texas San Antonio United States College of Computer Science and Technology China University of Petroleum [East China China School of Computer and Software Nanjing University of Information Science and Technology China

Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer from efficacy deterioration due to the system heterogeneity inherent in edge computing, especially in the presence of domain shifts across local data. In this paper, we propose a heterogeneous FL framework DapperFL, to enhance model performance across multiple domains. In DapperFL, we introduce a dedicated Model Fusion Pruning (MFP) module to produce personalized compact local models for clients to address the system heterogeneity challenges. The MFP module prunes local models with fused knowledge obtained from both local and remaining domains, ensuring robustness to domain shifts. Additionally, we design a Domain Adaptive Regularization (DAR) module to further improve the overall performance of DapperFL. The DAR module employs regularization generated by the pruned model, aiming to learn robust representations across domains. Furthermore, we introduce a specific aggregation algorithm for aggregating heterogeneous local models with tailored architectures and weights. We implement DapperFL on a real-world FL platform with heterogeneous clients. Experimental results on benchmark datasets with multiple domains demonstrate that DapperFL outperforms several state-of-the-art FL frameworks by up to 2.28%, while significantly achieving model volume reductions ranging from 20% to 80%. Our code is available at: https://***/jyzgh/DapperFL. © 2024 Neural information processing systems foundation. All rights reserved.

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INCL: An Effective Deep Learning Model to Identify Road Potholes by Using Image Based Neural Classification Logic

INCL: An Effective Deep Learning Model to Identify Road Poth...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Shasikala, Y. Kantha, M.Rajitha Dinesh, M. Nagamani, K. Bonde, A. Afanehsuha AdityaUniversity Departmentof Computer Applications Surampalem India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh Prakasam District India Dept of Vlsi Microelectronics Chennai India Qis College of Engineering and Technology Department of Mba Pondur Road Vengamukkapalem Ongole Prakasam India AndhraPradesh Prakasam District India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan Universiry of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

This paper presents a deep learning-based approach for detecting road potholes using image-based neural classification logic. The methodology involves collecting road images from publicly available datasets, particularly from Kaggle, and augmenting the dataset to improve model performance. Preprocessing steps such as resizing, standardizing, and annotating the images were employed to ensure consistency. A pre-trained Faster R-CNN model was fine-tuned for pothole detection, using convolutional neural networks (CNNs) to extract features and classify the road images into pothole and non-pothole categories. The model was trained using transfer learning and tested under varying road and environmental conditions, including day, night, dry, and wet scenarios. The model achieved an accuracy of 96.5% in dry daylight conditions, with a slightly lower performance of 85.4% during wet nighttime conditions. Additionally, precision and recall were evaluated, with the model achieving a precision of 95.3% and a recall of 97.6% for daytime conditions. The system was implemented in a real-time environment, allowing for real-time pothole detection and reporting through a mobile or web application. Future work will aim to improve performance under challenging conditions and enhance real-time deployment efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Shared Resource Entanglement Attacks against Serverless Computing

Shared Resource Entanglement Attacks against Serverless Comp...

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IEEE Conference on Communications and Network security (CNS)

作者： Yuwen Cui Mingkui Wei Zhuo Lu Yao Liu Department of Computer University of South Florida Department of Cyber Security Engineering George Mason University Department of Electrical Engineering University of South Florida

ISBN: (数字)9798350375961

ISBN: (纸本)9798350375978

Serverless computing has emerged as a popular paradigm for building and deploying applications in the cloud. At the heart of this paradigm lies the concept of serverless functions, which have gained significant popularity due to their simplicity, scalability, and cost-efficiency. However, running serverless services on the same physical machine can introduce security threats and risks, primarily when identical serverless functions belong to different tenants. In this paper, we investigate the resource consumption of serverless functions and propose a shared resource entanglement (SRE) threat model. The SRE threat model enables adversaries to infer sensitive data from a victim’s serverless function by intercepting and monitoring the host kernel interacting data patterns and the data load and write rate patterns. The experiment results demonstrate that adversaries can identify patterns in the data processing of coresident serverless functions using the SRE threat model. Also, this vulnerability could lead to the leakage of sensitive data in the Kubernetes platform.

关键词： Threat modeling Heart Scalability Buildings Serverless computing Interference Network security Data processing Kernel Monitoring

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A Framework for Securing Saudi Arabian Hospital Industry: Vision-2030 Perspective

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Intelligent Automation & Soft Computing 2023年第6期36卷 2773-2786页

作者： Hosam Alhakami Abdullah Baz Mohammad Al-shareef Rajeev Kumar Alka Agrawal Raees Ahmad Khan Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955SaudiArabia Department of Computer Engineering College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Security Forces Hospital Makkah21955Saudi Arabia Department of Computer Science and Engineering Babu Banarasi Das UniversityLucknow226028Uttar PradeshIndia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia

Recent transformation of Saudi Arabian healthcare sector into a reven-ue producing one has signaled several advancements in healthcare in the *** healthcare management into Smart hospital systems is one of *** hospital management systems which are breach-proof only can be termed as effective smart hospital *** the perspective of Saudi Vision-2030,many practitioners are trying to achieve a cost-effective hospital management sys-tem by using smart *** this row,the proposed framework posits the main objectives for creating smart hospital management systems that can only be acknowledged by managing the security of healthcare data and medical ***,the proposed framework will also be helpful in gaining satisfactory rev-enue from the healthcare sector by reducing the cost and time involved in mana-ging the smart hospital *** framework is based on a hybrid approach of three key methods which include:employing the Internet of Medical Things(IoMT)and blockchain methodologies for maintaining the security and privacy of healthcare data and medical practices,and using big data analytics methodol-ogy for raising the funds and revenue by managing the bulk volume of healthcare ***,the framework will also be helpful for both the patients and the doctors,thus enabling the Kingdom of Saudi Arabia(KSA)to meet its goals of Vision-2030 by ensuring low cost,yet credible,healthcare services.

关键词： Smart healthcare healthcare industry data security

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Heart Attack Prediction by Integrating Independent Component Analysis with Machine Learning Classifiers 17

Heart Attack Prediction by Integrating Independent Component...

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17th International Conference on Developments in eSystems engineering, DeSE 2024

作者： Nawar, Abbas Khalifa Jebur, Sabah Abdulazeez Jawad, Hasan Abdulrazzaq Jahefer, Mothefer Majeed Alkhazraji, Lafta Hussain, Abir Jaafar Dept of Computer Techniques Engineering Imam Alkadhim University College Baghdad Iraq Dept of Cyber Security Imam Alkadhim University College Baghdad Iraq Department of Medical Instrumentation Engineering Techniques Imam Ja'afar Al-Sadiq University Iraq Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350368697

Detecting heart attack conditions at an early stage is crucial for healthcare, making innovative solutions that contribute to prevention and overall health improvement imperative. Machine learning, as a subset of Artificial Intelligence, plays a pivotal role in heart attack analysis and prediction, leveraging its ability to process large datasets and identify complex patterns. This study introduces a new approach to heart attack prediction by incorporating Independent Component Analysis (ICA) with various ML classifiers, namely K-Nearest Neighbors, Gradient Boosting, Naive Bayes, Support Vector Machine, and Random Forest. The proposed model was evaluated on two benchmark datasets, achieving notable accuracy rates of 93.44% and 100% on the Cleveland dataset and heart disease dataset, respectively. In addition to accuracy, a comprehensive evaluation using various metrics, including F1-score, Area Under the Curve (AUC), Precision, Recall, and Error rate, is performed. Furthermore, the analysis of the AUC-ROC curve and confusion matrix provides valuable insights into the models' predictive capabilities. The findings underscore the effectiveness of ICA in feature extraction and highlight the remarkable potential of KNN as a robust classifier for heart attack prediction. © 2024 IEEE.

关键词： Adversarial machine learning

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Energy Harvesting and Smart Wireless Home Automation System using Internet of Things based Applications 3

Energy Harvesting and Smart Wireless Home Automation System ...

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

作者： Veeramuthu, A. Dinesh, M. Jayamani, K. Saravanan, L. Giri, Jayant Al Sherideh, Ala'a Sathyabama Institute of Science and Technology Department of Information Technology Chennai India Saveetha School of Engineering Department of Vlsi Microelectronics Chennai India Rajalakshmi Institute of Technology Chennai India Peri Institute of Technology Department of Ece Mannivakkam Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Zarqa University College of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331509675

A comprehensive approach to energy-efficient smart home automation leveraging IoT technologies. The Altair Smart Core IoT platform integrates with Arduino, enabling remote control and management of household devices. The developed EHSWH model introduces a five-layered system architecture, facilitating automation features such as main door control, temperature and humidity monitoring, smart bin management, smart water pump operation, and automatic light and door control. Users can interact with the system effortlessly through RESTful web services and dedicated Android/web applications, ensuring convenience and accessibility. Extensive experimentation using the ns3 software in Ubuntu OS demonstrates the superior performance of the proposed EHSWHs protocol across key metrics, including packet delivery ratio, network throughput, energy efficiency, average delay, and routing overhead, underscoring its effectiveness in optimizing IoT network performance and efficiency. © 2025 IEEE.

关键词： Web services

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Applied Aspects of Polyharmonic Functions for security and Reliability of Data Transmission 8

Applied Aspects of Polyharmonic Functions for Security and R...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Laptiev, Oleksandr Sobchuk, Andrii Ryzhov, Anton Perehuda, Oleh Tsygansvska, Iryna Kopytko, Serhii Taras Shevchenko National University of Kyiv Faculty of information technology Department of Cyber Security and Information Protection Kyiv Ukraine State University of Information and Communication Technologies Department of Information and Cyber Security Kyiv Ukraine Taras Shevchenko National University of Kyiv Faculty of Mechanics and Mathematics Department of General Mathematics Kyiv Ukraine Taras Shevchenko National University of Kyiv Faculty of Mechanics and Mathematics Department of Algebra and Computer Mathematics Kyiv Ukraine Lviv Polytechnic National University Department of Information Systems and Networks Lviv Ukraine

ISBN: (纸本)9798350354423

At present, when armed conflicts have intensified, and especially against the background of Russia's armed aggression against Ukraine, a new era of development of high-precision means of protection and weapons has begun. Their effectiveness is primarily determined by the state of radio-electronic technical means. These include means of communication and control, radar and hydroacoustics, telemetry, etc. Thus, the effectiveness of the solution of the tasks before the radio-electronic device depends on the class of signals used. In particular, the type of signal depends on the quality of the communication, the range, the resolution capabilities according to various parameters, the probability of detection, the quality of control, stealth, etc. But the detection of hidden signals requires high-quality signal processing with consistent mathematical analysis of the signal. This requires a powerful mathematical apparatus. As an example of signal processing, the article discusses methods of using signal transformation using polyharmonic operators. The transformation of signals by two types of operators is presented: the Abel-Poisson operator and the Weierstrass operator. The simulation results proved the advantages of Weierstrass operators for signal transformation. © 2024 IEEE.

关键词： Mathematical transformations

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