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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Wind Power Prediction Based on Machine Learning and Deep Learning Models

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computers, Materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek Mahmoud Y.Shams Ahmed M.Elshewey El-Sayed M.El-kenawy Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

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Data Augmentation and Random Multi-Model Deep Learning for Data Classification

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computers, Materials & Continua 2023年第3期74卷 5191-5207页

作者： Fatma Harby Adel Thaljaoui Durre Nayab Suliman Aladhadh Salim EL Khediri Rehan Ullah Khan Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesEgypt Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar25120Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Sciences Faculty of Sciences of GafsaUniversity of GafsaGafsaTunisia

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML *** increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen *** learning(DL)models have a lot of parameters,and they frequently ***,to avoid overfitting,data plays a major role to augment the latest improvements in ***,reliable data collection is a major limiting ***,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in *** this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for *** present a methodology for using Generative Adversarial Networks(GANs)to generate images for data *** experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model *** across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

关键词： Data augmentation generative adversarial networks classification machine learning random multi-model deep learning

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SEF:A Smart and Energy-Aware Forwarding Strategy for NDN-Based Internet of Healthcare

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computers, Materials & Continua 2024年第12期81卷 4625-4658页

作者： Naeem Ali Askar Adib Habbal Hassen Hamouda Abdullah Mohammad Alnajim Sheroz Khan Department of Computer Engineering Faculty of EngineeringKarabük UniversityKarabük78050Türkiye Department ofManagement Information Systems College of Business AdministrationMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Electrical Engineering College of Engineering and Information TechnologyOnaizah CollegesQassim51911Saudi Arabia

Named Data Networking(NDN)has emerged as a promising communication paradigm,emphasizing content-centric access rather than location-based *** model offers several advantages for Internet of Healthcare Things(IoHT)environments,including efficient content distribution,built-in security,and natural support for mobility and ***,existing NDN-based IoHT systems face inefficiencies in their forwarding strategy,where identical Interest packets are forwarded across multiple nodes,causing broadcast storms,increased collisions,higher energy consumption,and *** issues negatively impact healthcare system performance,particularly for individuals with disabilities and chronic diseases requiring continuous *** address these challenges,we propose a Smart and Energy-Aware Forwarding(SEF)strategy based on reinforcement learning for NDN-based *** SEF strategy leverages the geographical distance and energy levels of neighboring nodes,enabling devices to make more informed forwarding decisions and optimize next-hop *** approach reduces broadcast storms,optimizes overall energy consumption,and extends network *** system model,which targets smart hospitals and monitoring systems for individuals with disabilities,was examined in relation to the proposed *** SEF strategy was then implemented in the NS-3 simulation environment to assess its performance in healthcare *** demonstrated that SEF significantly enhanced NDN-based IoHT ***,it reduced energy consumption by up to 27.11%,82.23%,and 84.44%,decreased retrieval time by 20.23%,48.12%,and 51.65%,and achieved satisfaction rates that were approximately 0.69 higher than those of other strategies,even in more densely populated *** forwarding strategy is anticipated to substantially improve the quality and efficiency of NDN-based IoHT systems.

关键词： Energy efficient forwarding strategy health information system internet technologies IoHT people with disabilities reinforcement learning Q-learning

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Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

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computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt Abdulwahab Alazeb Naif Al Mudawi Mohammad Alonazi Nouf Abdullah Almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： Semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter SURF

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Blockchain technology to Enhance Performance of Drugs Supply Chain in the Era of Digital Transformation in Egypt 3rd

Blockchain Technology to Enhance Performance of Drugs Supply...

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3rd World Conference on Internet of Things: Applications and Future, ITAF 2023

作者： Moussa, Aya Mohammed A. El-Gamal, Fatma El-Zahraa A. El Fetouh, Ahmed Saleh A. Business Information Technology Program Faculty of Computer and Information Mansoura University Mansoura Egypt Information Technology Department Faculty of Computer and Information Mansoura University Mansoura Egypt Delta Higher Institute for Management and Accounting Information Systems Mansoura Egypt

ISBN: (纸本)9789819947638

Medicine is one of the products that must be delivered to the patient safely, undamaged, and during its validity period. However, the risks of the drug supply chain have become increasingly apparent. To address these risks and due to the rapid advances in the technology field, various digital transformation trials around the globe start to be considered in order to replace the traditional drug supply chain until its delivery to the patient. Therefore, the aim of this paper is to contribute in the digital transformation goal of the drug supply chain in Egypt. Thus, this paper proposes a trusted automatic system, based on the blockchain and Internet of Things (IoT) technologies to enhance the performance of the drug supply chain and accordingly make the drug manufacturing and distribution system in Egypt more efficient and reliable. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Internet of things

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PPQSort: Pattern Parallel Quicksort 15th

PPQSort: Pattern Parallel Quicksort

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Hévr, Gabriel Šimeček, Ivan Department of Computer Systems Faculty of Information Technology Czech Technical University in Prague Thákurova 9 Prague16000 Czech Republic

ISBN: (纸本)9783031856969

This paper presents PPQSort (Pattern Parallel Quicksort), a new parallel quicksort algorithm that provides high performance and ease of use. PPQSort uses C++ threads for parallelization, achieving efficient sorting without external libraries and allowing seamless integration across different computing environments. This paper describes novel quicksort optimizations, including branchless partitioning and their efficient parallel implementation. PPQSort is compared with existing parallel quicksort algorithms on different machines and with different input data. Experimental evaluation results demonstrate that PPQSort is fast and robust, consistently outperforming the fastest available parallel quicksort implementations for almost all inputs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： C++ (programming language)

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Machine Learning-Based Calibration Approaches for Single-Beam and Multiple-Beam Distance Sensors

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IEEE Sensors Journal 2024年第1期24卷 975-983页

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Biernacki, Piotr Ziebinski, Adam National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice44-100 Poland Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice44-100 Poland

Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have developed various methods to calibrate distance sensors;however, they mainly focus on using external information for calibration, which is inefficient and computationally expensive. To alleviate the above problems, we propose machine learning (ML)-based calibration approaches for distance sensors. We develop two calibration mechanisms, where the naive mechanism directly predicts the true distance, while the residual mechanism predicts the residual for calibration. The conventional ML regression algorithms are then adopted to predict the true distance or calibration residual. Experimental results show that the designed residual calibration mechanism outperforms the naive mechanism by reducing the root mean square error (RMSE) by 92.2% and mean absolute error (MAE) by 93.3% in the fitting task. In prediction, the proposed 2-D-residual mechanism achieves an improvement of 78.6% and 65.7% compared to the state-of-the-art method. The experimental results show the effectiveness and generalization of the proposed ML-based calibration method, especially for the 2-D mechanism, which achieves the highest robustness in all fitting and prediction tasks. © 2001-2012 IEEE.

关键词： Calibration

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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An Optimal Optimization of Software Development Cost Estimation Using Genetic Algorithm 4

An Optimal Optimization of Software Development Cost Estimat...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Amarif, Mabroukah Owaydat, Salha Sebha University of Libya Faculty of Information Technology Dept. of Information Systems Sebha Libya Sebha University of Libya Faculty of Sciences Dept. of Computer Sciences Sebha Libya

ISBN: (纸本)9798350372632

Estimating effort allows managers and software engineers to accurately anticipate, forecast, and quote schedule, budget, and manpower requirements. Determining estimated project cost, duration, and maintenance efforts well in advance Of the development stages is the biggest defiance to be attained for software projects. Formal models for cost estimation, such as the Constructive Cost Model (COCOMO) are limited by their inability to manage uncertainty in software projects early development cycle. This research presents an optimal optimization of software development cost estimation using genetic algorithm. It provides a fine solution to set the uncertain and ambiguous properties of software factors. It adopts COCOMO II model formulas and manages fine-tuning parameters for accurate effort and scheduled time software cost estimation. An experiment has been carried out using a NASA data set in order to improve the proposed algorithm. The experimental results show a significant optimization for both software effort up to 97.27% accuracy and scheduled time up to 98.88%. This research has emphasized that applying the genetic algorithm with fine-tuning parameters of COCOMO II model definitely improve the software development cost estimation. © 2024 IEEE.

关键词： Genetic algorithms

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