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A Hybrid and Lightweight Device-to-Server Authentication Technique for the Internet of Things

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Computers, Materials & Continua 2024年第3期78卷 3805-3823页

作者： shaha Al-Otaibi rahim Khan Hashim Ali Aftab Ahmed Khan Amir Saeed Jehad Ali Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Abdul Wali Khan University MardanMardan23200Pakistan Department of Computer Science and IT UET PeshawarJalozai Campus PeshawarPeshawar24240Pakistan Department of AI Convergence Network Ajou UniversitySuwon16499South Korea

The Internet of Things(IoT)is a smart networking infrastructure of physical devices,i.e.,things,that are embedded with sensors,actuators,software,and other technologies,to connect and share data with the respective server *** IoTs are cornerstones in different application domains,the device’s authenticity,i.e.,of server(s)and ordinary devices,is the most crucial issue and must be resolved on a priority ***,various field-proven methodologies were presented to streamline the verification process of the communicating devices;however,location-aware authentication has not been reported as per our knowledge,which is a crucial metric,especially in scenarios where devices are *** paper presents a lightweight and location-aware device-to-server authentication technique where the device’s membership with the nearest server is subjected to its location information along with other ***,Media Access Control(MAC)address and Advance Encryption Scheme(AES)along with a secret shared key,i.e.,λ_(i) of 128 bits,have been utilized by Trusted Authority(TA)to generate MaskIDs,which are used instead of the original ID,for every device,i.e.,server and member,and are shared in the offline ***,TA shares a list of authentic devices,i.e.,server S_(j) and members C_(i),with every device in the IoT for the onward verification process,which is required to be executed before the initialization of the actual communication ***,every device should be located such that it lies within the coverage area of a server,and this location information is used in the authentication process.A thorough analytical analysis was carried out to check the susceptibility of the proposed and existing authentication approaches against well-known intruder attacks,i.e.,man-in-the-middle,masquerading,device,and server impersonations,etc.,especially in the IoT ***,proposed authentication and existing state-of-the-art approaches have been s

关键词： Internet of things authenticity security location communication

Hybrid immunizing solution for job recommender system

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Frontiers of Computer Science 2017年第3期11卷 511-527页

作者： shaha AL-OTAIBI Mourad YKHLEF Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Two traditional recommendation techniques, content-based and collaborative filtering （CF）, have been widely used in a broad range of domain areas. Both meth- ods have their advantages and disadvantages, and some of the defects can be resolved by integrating both techniques in a hybrid model to improve the quality of the recommendation. In this article, we will present a problem-oriented approach to design a hybrid immunizing solution for job recommen- dation problem from applicant＇s perspective. The proposed approach aims to recommend the best chances of opening jobs to the applicant who searches for job. It combines the artificial immune system （AIS）, which has a powerful explo- ration capability in polynomial time, with the collaborative filtering, which can exploit the neighbors＇ interests. We will discuss the design issues, as well as the hybridization process that should be applied to the problem. Finally, experimental studies are conducted and the results show the importance of our approach for solving the job recommendation problem.

关键词： content-based collaborative filtering (CF) hy- bridization computational intelligence (CI) artificial im- mune system (AIS) clonal selection correlation-based simi- larity

Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

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Computers, Materials & Continua 2022年第9期72卷 4541-4555页

作者： Manar Ahmed Hamza Noha Negm shaha Al-Otaibi Amel AAlhussan Mesfer Al Duhayyim Fuad Ali Mohammed Al-Yarimi Mohammed rizwanullah Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz University16278AlKharjSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayilAsir62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz University16278Saudi Arabia

Machine learning (ML) becomes a familiar topic among decisionmakers in several domains, particularly healthcare. Effective design of MLmodels assists to detect and classify the occurrence of diseases using healthcaredata. Besides, the parameter tuning of the ML models is also essentialto accomplish effective classification results. This article develops a novelred colobuses monkey optimization with kernel extreme learning machine(rCMO-KELM) technique for epileptic seizure detection and *** proposed rCMO-KELM technique initially extracts the chaotic, time,and frequency domain features in the actual EEG signals. In addition, the minmax normalization approach is employed for the pre-processing of the EEGsignals. Moreover, KELM model is used for the detection and classificationof epileptic seizures utilizing EEG signal. Furthermore, the rCMO techniquewas utilized for the optimal parameter tuning of the KELM technique insuch a way that the overall detection outcomes can be considerably *** experimental result analysis of the rCMO-KELM technique has beenexamined using benchmark dataset and the results are inspected under severalaspects. The comparative result analysis reported the better outcomes of therCMO-KELM technique over the recent approaches with the accuy of 0.956.

关键词： Epileptic seizures eeg signals machine learning kelm parameter tuning rcmo algorithm

Privacy Preserving Image Encryption with Deep Learning Based IoT Healthcare Applications

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Computers, Materials & Continua 2022年第10期73卷 1159-1175页

作者： Mohammad Alamgeer Saud S.Alotaibi shaha Al-Otaibi Nazik Alturki Anwer Mustafa Hilal Abdelwahed Motwakel Ishfaq Yaseen Mohamed I.Eldesouki Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge ***,data encryption,and deep learning(DL)models can be utilized to design efficient security solutions for IoT healthcare *** this aspect,this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning(BPPIEODL)technique for IoT healthcare *** proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud *** proposed BPPIE-ODL technique encompasses the design of dragonfly algorithm(DFA)with signcryption technique to encrypt the medical images captured by the IoT ***,blockchain(BC)can be utilized as a distributed data saving approach for generating a ledger,which permits access to the users and prevents third party’s access to encrypted *** addition,the classification process includes SqueezeNet based feature extraction,softmax classifier(SMC),and Nadam based hyperparameter *** usage of Nadam model helps to optimally regulate the hyperparameters of the SqueezeNet *** examining the enhanced encryption as well as classification performance of the BPPIE-ODL technique,a comprehensive experimental analysis is carried *** simulation outcomes demonstrate the significant performance of the BPPIE-ODL technique on the other techniques with increased precision and accuracy of 0.9551 and 0.9813 respectively.

关键词： Internet of things healthcare decision making privacy preserving blockchain deep learning

Deep Learning Based License Plate Number recognition for Smart Cities

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Computers, Materials & Continua 2022年第1期70卷 2049-2064页

作者： T.Vetriselvi E.Laxmi Lydia Sachi Nandan Mohanty Eatedal Alabdulkreem shaha Al-Otaibi Amal Al-rasheed romany F.Mansour Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTrichy620002India Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science&Engineering Vardhaman College of Engineering(Autonomous)Hyderabad501218India Department of Informatics and Computing Singidunum UniversitySerbia Computer Science Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended *** controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city *** the same time,Vehicle License Plate Number recognition(VLPNr)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic *** VLPNr is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number *** current research paper presents an effective Deep Learning(DL)-based VLPNr called DLVLPNr model to identify and recognize the alphanumeric characters present in license *** proposed model involves two main stages namely,license plate detection and Tesseract-based character *** detection of alphanumeric characters present in license plate takes place with the help of fast rCNN with Inception V2 ***,the characters in the detected number plate are extracted using Tesseract Optical Character recognition(OCr)*** performance of DL-VLPNr model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

关键词： Deep learning smart city tesseract computer vision vehicle license plate recognition

An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques

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Computers, Materials & Continua 2023年第2期74卷 3315-3332页

作者： Mesfer Al Duhayyim Saud S.Alotaibi shaha Al-Otaibi Fahd N.Al-Wesabi Mahmoud Othman Ishfaq Yaseen Mohammed rizwanullah Abdelwahed Motwakel Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science and Arts at MuhayelKing Khalid UniversityMuhayel AseerSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing ***,waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management *** study concentrates on the design of hazardous waste detection and classification using ensemble learning(HWDC-EL)technique to reduce toxicity and improve human *** goal of the HWDC-EL technique is to detect the multiple classes of wastes,particularly hazardous and non-hazardous *** HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns(DLBP),EfficientNet,and *** addition,the flower pollination algorithm(FPA)based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 ***,a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine(SVM),extreme learning machine(ELM),and gradient boosting tree(GBT).The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%.

关键词： Hazardous waste image classification ensemble learning deep learning intelligent models human health weighted voting model

Optimal Deep Convolutional Neural Network for Vehicle Detection in remote Sensing Images

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Computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： Saeed Masoud Alshahrani Saud S.Alotaibi shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(rSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue *** recognition from rSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

Improved Chameleon Swarm Optimization-Based Load Scheduling for IoT-Enabled Cloud Environment

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Computer Systems Science & Engineering 2023年第8期46卷 1371-1383页

作者： Manar Ahmed Hamza shaha Al-Otaibi Sami Althahabi Jaber S.Alzahrani Abdullah Mohamed Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art atMahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecaa24382Saudi Arabia research Centre Future University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Internet of things(IoT)and cloud computing(CC)becomes widespread in different application domains such as business,e-commerce,healthcare,*** recent developments of IoT technology have led to an increase in large amounts of data from various *** IoT enabled cloud environment,load scheduling remains a challenging process which is applied for ensuring network stability with maximum resource *** load scheduling problem was regarded as an optimization problem that is solved by *** this view,this study develops a new Circle Chaotic Chameleon Swarm Optimization based Load Scheduling(C3SOA-LS)technique for IoT enabled cloud *** proposed C3SOA-LS technique intends to effectually schedule the tasks and balance the load uniformly in such a way that maximum resource utilization can be ***,the presented C3SOA-LS model involves the design of circle chaotic mapping(CCM)with the traditional chameleon swarm optimization(CSO)algorithm for improving the exploration process,shows the novelty of the *** proposed C3SOA-LS model computes an objective with the minimization of energy consumption and *** experimental outcome implied that the C3SOA-LS model has showcased improved performance and uniformly balances the load over other approaches.

关键词： Cloud environment load scheduling energy consumption internet of things metaheuristics

An Optimal Big Data Analytics with Concept Drift Detection on High-Dimensional Streaming Data

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Computers, Materials & Continua 2021年第9期68卷 2843-2858页

作者： romany F.Mansour.shaha Al-Otaibi Amal Al-rasheed Hanan Aljuaid Irina V.Pustokhina Denis A.Pustokhin Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Big data streams started becoming ubiquitous in recent years,thanks to rapid generation of massive volumes of data by different *** is challenging to apply existing data mining tools and techniques directly in these big data *** the same time,streaming data from several applications results in two major problems such as class imbalance and concept *** current research paper presents a new Multi-Objective Metaheuristic Optimization-based Big Data Analytics with Concept Drift Detection(MOMBD-CDD)method on High-Dimensional Streaming *** presented MOMBD-CDD model has different operational stages such as pre-processing,CDD,and ***-CDD model overcomes class imbalance problem by Synthetic Minority Over-sampling Technique(SMOTE).In order to determine the oversampling rates and neighboring point values of SMOTE,Glowworm Swarm Optimization(GSO)algorithm is ***,Statistical Test of Equal Proportions(STEPD),a CDD technique is also ***,Bidirectional Long Short-Term Memory(Bi-LSTM)model is applied for *** order to improve classification performance and to compute the optimum parameters for Bi-LSTM model,GSO-based hyperparameter tuning process is carried *** performance of the presented model was evaluated using high dimensional benchmark streaming datasets namely intrusion detection(NSL KDDCup)dataset and ECUE spam *** extensive experimental validation process confirmed the effective outcome of MOMBD-CDD *** proposed model attained high accuracy of 97.45%and 94.23%on the applied KDDCup99 Dataset and ECUE Spam datasets respectively.

关键词： Streaming data concept drift classification model deep learning class imbalance data