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Optimal Wavelet Neural Network-Based Intrusion Detection in Internet of Things Environment

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Computers, Materials & Continua 2023年第5期75卷 4467-4483页

作者： heba G.mohamed Fadwa alrowais Mohammed Abdullah al-Hagery Mesfer al Duhayyim anwer Mustafa Hilal Abdelwahed Motwakel Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box84428Riyadh 11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box84428Riyadh 11671Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

As the Internet of Things(IoT)endures to develop,a huge count of data has been created.An IoT platform is rather sensitive to security challenges as individual data can be leaked,or sensor data could be used to cause accidents.As typical intrusion detection system(IDS)studies can be frequently designed for working well on databases,it can be unknown if they intend to work well in altering network environments.Machine learning(ML)techniques are depicted to have a higher capacity at assisting mitigate an attack on IoT device and another edge system with reasonable accuracy.This article introduces a new Bird Swarm algorithm with Wavelet Neural Network for Intrusion Detection(BSAWNN-ID)in the IoT platform.The main intention of the BSAWNN-ID algorithm lies in detecting and classifying intrusions in the IoT platform.The BSAWNN-ID technique primarily designs a feature subset selection using the coyote optimization algorithm(FSS-COA)to attain this.Next,to detect intrusions,the WNN model is utilized.At last,theWNNparameters are optimally modified by the use of BSA.Awidespread experiment is performed to depict the better performance of the BSAWNNID technique.The resultant values indicated the better performance of the BSAWNN-ID technique over other models,with an accuracy of 99.64%on the UNSW-NB15 dataset.

关键词： Internet of things wavelet neural network security intrusion detection machine learning

Artificial algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

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Computer Systems Science & Engineering 2023年第8期46卷 1293-1310页

作者： Mesfer al Duhayyim heba G.mohamed Fadwa alrowais Fahd N.al-Wesabi anwer Mustafa Hilal Abdelwahed Motwakel Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityRiyadhSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science and Art at MuhaeylKing Khalid UniversityAbhaSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The Internet of Things(IoT)has gained more popularity in research because of its large-scale challenges and implementation.But security was the main concern when witnessing the fast development in its applications and size.It was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer threats.Additionally,machine learning(ML)techniques optimally use a colossal volume of data generated by IoT devices.Deep Learning(DL)related systems were modelled for attack detection in IoT.But the current security systems address restricted attacks and can be utilized outdated datasets for evaluations.This study develops an Artificial algae Optimization algorithm with Optimal Deep Belief Network(AAA-ODBN)Enabled Ransomware Detection in an IoT environment.The presented AAAODBN technique mainly intends to recognize and categorize ransomware in the IoT environment.The presented AAA-ODBN technique follows a three-stage process:feature selection,classification,and parameter tuning.In the first stage,the AAA-ODBN technique uses AAA based feature selection(AAA-FS)technique to elect feature subsets.Secondly,the AAA-ODBN technique employs the DBN model for ransomware detection.At last,the dragonfly algorithm(DFA)is utilized for the hyperparameter tuning of the DBN technique.A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN algorithm.The experimental values indicate the significant outcome of the AAA-ODBN model over other models.

关键词： Internet of things deep learning cybersecurity ransomware detection feature selection

Prevelance of Bovine Cysticercosis in Egypt and the Cysticidal Effect of Two Extracts Obtained from Balanites aegyptiaca and Moringa oleifera on Mice Model Affected with T. saginata Cysticerci

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Open Journal of Animal Sciences 2024年第2期14卷 39-55页

作者： Omnia M. Kandil Noha M. F. Hassan Doaa Sedky Hatem A. Shalaby heba M. Ashry Nadia M. T. Abu El Ezz Sahar M. Kandeel mohamed S. Abdelfattah L. Ying Ebtesam M. al-Olayan Parasitology and Animal Diseases Department Veterinary Research Institute National Research Centre Dokki Cairo Egypt Chemistry of Natural Compounds Department Pharmaceutical and Drugs Industries Research Institute National Research Centre Dokki Cairo Egypt Chemistry Department Faculty of Science Helwan University Cairo Egypt State Key Laboratory of Plateau Ecology and Agriculture Qinghai University Xining China College of Agriculture and Animal Husbandry Qinghai University Xining China Department of Zoology Collage of Science King Saud University Riyadh Saudi Arabia

The aim of the present study was to determine the prevalence of bovine cysticercosis in both cattle and buffloas, in Egypt and to assess the cysticidal efficacy of Balanites aegyptiaca fruits (B. aegyptiaca) and Moringa oleifera seeds (M. oleifera) extracts in experimentally infected mice. The study detected the level of tumor necrosis factor (TNF-α) to monitor the immune and inflammatory responses of experimentally infected mice. Through meat inspection, a total number of 2125 male bovine, 2 to 5 years old, (1125 cattle and 1000 buffloes) were examined under the authority of albsatine and alwaraq official abattoirs in Cairo Governorate, Egypt covering the period extended from March 2022 to April 2023. The overall prevalence of the disease among bovine was 7.8% (6.31% of cattle and 9.5% of buffloes). Besides, B. aegyptiaca and M. oleifera extracts showed cysticidal activity in experimentally infected mice. A decrease in the numbers of cysticerci was found in all treated mice groups, and up to 88% reduction was achieved in the B. aegyptiaca-treated group;higher than that was recorded in both M. oleifera (72.23%) and albendazole-treated ones (80.56%). Postmortem findings proved that M. oleifera and B. aegyptiaca reduced cysticerci numbers comparable to a commercial anthelmintic. The study showed a significant decrease (P 0.001) in TNF-α levels after treatment with Balanites and Moringa extracts, compared with the untreated control and the albendazole-treated groups.

关键词： Prevalence Balanites aegyptiaca Moringa oleifera Mice T. saginata Cysticerci

Intelligent Deep Convolutional Neural Network Based Object DetectionModel for Visually Challenged People

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Computer Systems Science & Engineering 2023年第9期46卷 3191-3207页

作者： S.Kiruthika Devi Amani Abdulrahman albraikan Fahd N.al-Wesabi mohamed K.Nour Ahmed ashour anwer Mustafa Hilal Department of Computing Technology SRM Institute of Science and TechnologyIndia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Engineering Mathematics and Physics Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial Intelligence(AI)and Computer Vision(CV)advancements have led to many useful methodologies in recent years,particularly to help visually-challenged people.Object detection includes a variety of challenges,for example,handlingmultiple class images,images that get augmented when captured by a camera and so on.The test images include all these variants as well.These detection models alert them about their surroundings when they want to walk independently.This study compares four CNN-based pre-trainedmodels:ResidualNetwork(ResNet-50),Inception v3,DenseConvolutional Network(DenseNet-121),and SqueezeNet,predominantly used in image recognition applications.Based on the analysis performed on these test images,the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and speed.To further improve the performance of the Inception v3 model,the thermal exchange optimization(TEO)algorithm is applied to tune the hyperparameters(number of epochs,batch size,and learning rate)showing the novelty of the work.Better accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer,hyperparameter optimizer,and factorization approach.Additionally,Inception V3 can handle images of different sizes.This makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things(IoT)-based devices.

关键词： Pre-trained models object detection visually challenged people deep learning Inception V3 DenseNet-121

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

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Computers, Materials & Continua 2023年第1期74卷 607-621页

作者： anwer Mustafa Hilal Aisha Hassan Abdalla Hashim heba G.mohamed mohamed K.Nour Mashael M.Asiri ali M.al-Sharafi Mahmoud Othman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University Malaysia 53100 Kuala LumpurMalaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era.Malicious Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems.This may result in compromised security of the systems,scams,and other such cyberattacks.These attacks hijack huge quantities of the available data,incurring heavy financial loss.At the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify them.With this motivation,the current article develops an Artificial Fish Swarm algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)model.The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs.To attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique.In addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious URLs.Finally,AFSA is applied to the proposed model to enhance the efficiency of GRU model.The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository.The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

关键词： Malicious URL cybersecurity deep learning machine learning metaheuristics gated recurrent unit

Water Wave Optimization with Deep Learning Driven Smart Grid Stability Prediction

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Computers, Materials & Continua 2022年第12期73卷 6019-6035页

作者： anwer Mustafa Hilal Aisha Hassan Abdalla Hashim heba G.mohamed Mohammad alamgeer mohamed K.Nour Anas Abdelrahman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariJhnujhunuRajasthanIndia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Mechanical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control systems,and communication technologies.In SGs,user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity consumption.Since the entire procedure is valued on the basis of time,it is essential to perform adaptive estimation of the SG’s stability.Recent advancements inMachine Learning(ML)andDeep Learning(DL)models enable the designing of effective stability prediction models in SGs.In this background,the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction(WWOODL-SGSP)model.The aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient manner.To attain this,the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform level.Then,WWO algorithm is applied to choose an optimal subset of features from the pre-processed data.Next,Deep Belief Network(DBN)model is followed to predict the stability level of SGs.Finally,Slime Mold algorithm(SMA)is exploited to fine tune the hyperparameters involved in DBN model.In order to validate the enhanced performance of the proposedWWOODL-SGSP model,a wide range of experimental analyses was performed.The simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches.

关键词： Smart grid stability prediction deep learning energy systems machine learning metaheursitics

Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

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Computer Systems Science & Engineering 2023年第5期45卷 2033-2047页

作者： anwer Mustafa Hilal Aisha Hassan Abdalla Hashim heba G.mohamed Lubna A.alharbi mohamed K.Nour Abdullah mohamed Ahmed S.almasoud Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk47512Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia

Recent developments on Internet and social networking have led to the growth of aggressive language and hate speech.Online provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize CB.Current advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social networks.In this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(SHODLCS)model for OSN.The presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the OSN.For achieving this,the SHODLCS model involves data pre-processing and TF-IDF based feature extraction.In addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of CB.Finally,the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier performance.The experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches.

关键词： Cybersecurity cyberbullying online social network deep learning spotted hyena optimizer

Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

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Computers, Materials & Continua 2022年第5期71卷 3969-3983页

作者： CSS Anupama T.J.Benedict Jose heba FEid Nojood O aljehane Fahd N.al-Wesabi Marwa Obayya anwer Mustafa Hilal Department Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Faculty of Science AL-Azhar UniversityCairo11651Egypt Faculty of Computers and Information Technology University of Tabuk47512Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid University62529Saudi Arabia&Faculty of Computer and ITSana’a University31220Yemen Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman University11564Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharj16278Saudi Arabia

Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or tissues.Biomedical image processing concepts are identical to biomedical signal processing,which includes the investigation,improvement,and exhibition of images gathered using x-ray,ultrasound,MRI,etc.At the same time,cervical cancer becomes a major reason for increased women’s mortality rate.But cervical cancer is an identified at an earlier stage using regular pap smear images.In this aspect,this paper devises a new biomedical pap smear image classification using cascaded deep forest(BPSIC-CDF)model on Internet of Things(IoT)environment.The BPSIC-CDF technique enables the IoT devices for pap smear image acquisition.In addition,the pre-processing of pap smear images takes place using adaptive weighted mean filtering(AWMF)technique.Moreover,sailfish optimizer with Tsallis entropy(SFO-TE)approach has been implemented for the segmentation of pap smear images.Furthermore,a deep learning based Residual Network(ResNet50)method was executed as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear images.In order to showcase the improved diagnostic outcome of the BPSICCDF technique,a comprehensive set of simulations take place on Herlev database.The experimental results highlighted the betterment of the BPSICCDF technique over the recent state of art techniques interms of different performance measures.

关键词： Biomedical imaging pap smear images internet of things deep learning cervical cancer disease diagnosis

Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

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Computers, Materials & Continua 2022年第12期73卷 5011-5024页

作者： Mesfer al Duhayyim heba G.mohamed Saud S.alotaibi Hany Mahgoub Abdullah mohamed Abdelwahed Motwakel Abu Sarwar Zamani mohamed I.Eldesouki Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl KharjSaudi Arabia

Cyberbullying(CB)is a challenging issue in social media and it becomes important to effectively identify the occurrence of CB.The recently developed deep learning(DL)models pave the way to design CB classifier models with maximum performance.At the same time,optimal hyperparameter tuning process plays a vital role to enhance overall results.This study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification(TLGODL-CBC)model in Social Media.The proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media context.Initially,the input data is cleaned and pre-processed to make it compatible for further processing.Followed by,independent recurrent autoencoder(IRAE)model is utilized for the recognition and classification of CBs.Finally,the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the work.To assuring the improved outcomes of the TLGODLCBC approach,a wide range of simulations are executed and the outcomes are investigated under several aspects.The simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches.

关键词： Social media deep learning cyberbullying cybersecurity hyperparameter optimization