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Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks

Computers, Materials & Continua 2022年第3期70卷 6257-6270页

作者： Mesfer Al Duhayyim Haya Mesfer Alshahrani Fahd NAl-Wesabi mohammed Alamgeer Anwer Mustafa Hilal mohammed rizwanullah Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Cybersecurity encompasses various elements such as strategies,policies,processes,and techniques to accomplish availability,confidentiality,and integrity of resource processing,network,software,and data from *** this scenario,the rising popularity of Online Social Networks(OSN)is under threat from spammers for which effective spam bot detection approaches should be *** studies have developed different approaches for the detection of spam bots in *** those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning(DL)models needs to be *** this motivation,the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as *** proposed SBD-HDL technique focuses on the detection of spam bots that exist in *** technique has different stages of operations such as pre-processing,classification,and parameter ***,SBD-HDL technique hybridizes Graph Convolutional Network(GCN)with Recurrent Neural Network(RNN)model for spam bot classification *** order to enhance the detection performance of GCN-RNN model,hyperparameters are tuned using Lion Optimization Algorithm(LOA).Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work,a first-of-its-kind in this *** experimental validation of the proposed SBD-HDL technique,conducted upon benchmark dataset,established the supremacy of the technique since it was validated under different measures.

关键词： Cybersecurity spam bot data classification social networks twitter deep learning

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Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

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Computers, Materials & Continua 2023年第7期76卷 975-992页

作者： Hadeel Alsolai mohammed rizwanullah Mashael Maashi Mahmoud Othman Amani A.Alneil Amgad Atta Abdelmageed Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityP.O.box 103786Riyadh11543Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Soil classification is one of the emanating topics and major concerns in many *** the population has been increasing at a rapid pace,the demand for food also increases *** approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil *** comes a demand for computer-related soil classification methods to support *** study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)*** presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision *** the presented GBODL-ASC technique,three major processes are *** the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature *** soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)*** design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,*** demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five *** simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%.

关键词： Soil classification earth sciences machine learning parameter optimization metaheuristics

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Deep Learning with Image Classification Based Secure CPS for Healthcare Sector

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Computers, Materials & Continua 2022年第8期72卷 2633-2648页

作者： Ahmed S.Almasoud Abdelzahir Abdelmaboud Faisal S.Alsubaei Manar Ahmed Hamza Ishfaq Yaseen mohammed Abaker Abdelwahed Motwakel mohammed rizwanullah Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia Department of Information Systems College of Science and ArtsKing Khalid UniversityMahayil Asir62529Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Computer Science Community CollegeKing Khalid UniversityMahayil Asir62529Saudi Arabia

Cyber-Physical System(CPS)involves the combination of physical processes with computation and communication *** recent advancementsmade in cloud computing,Wireless Sensor Network(WSN),healthcare sensors,*** to develop CPS as a proficient model for healthcare applications especially,home patient *** several techniques have been proposed earlier related to CPS structures,only a handful of studies has focused on the design of CPS models for health care ***,the proposal for a dedicated CPS model for healthcare sector necessitates a significant interest to ensure data *** overcome the challenges,the current research paper designs a Deep Learning-based Intrusion Detection and Image Classification for Secure CPS(DLIDIC-SCPS)model for healthcare *** aim of the proposed DLIDIC-SCPS model is to achieve secure image transmission and image classification process for CPS in healthcare ***,data acquisition takes place with the help of sensors and detection of intrusions is performed using Fuzzy Deep Neural Network(FDNN)***,Multiple Share Creation(MSC)approach is used to create several shares of medical image so as to accomplish ***,blockchain is employed as a distributed data storage entity to create a ledger that provides access to the *** image classification,Inception v3 with Fuzzy Wavelet Neural Network(FWNN)is utilized that diagnose the disease from the applied medical ***,Salp Swarm Algorithm(SSA)is utilized to fine tune the parameters involved in WNN model,thereby boosting its classification performance.A wide range of simulations was carried out to highlight the superiority of the proposed DLIDIC-SCPS *** simulation outcomes confirm that DLIDIC-SCPS approach demonstrates promising results in terms of security,privacy,and image classification outcomes over recent state-of-the-art techniques.

关键词： Cyber Physical Systems healthcare cybersecurity deep learning share creation image classification

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Hybrid Deep Learning Enabled Intrusion Detection in Clustered IIoT Environment

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Computers, Materials & Continua 2022年第8期72卷 3763-3775页

作者： Radwa Marzouk Fadwa Alrowais Noha Negm Mimouna Abdullah Alkhonaini Manar Ahmed Hamza mohammed rizwanullah Ishfaq Yaseen Abdelwahed Motwakel 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 Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Industrial Internet of Things(IIoT)is an emerging field which connects digital equipment as well as services to physical *** detection systems(IDS)can be designed to protect the system from intrusions or *** this view,this paper presents a novel hybrid deep learning with metaheuristics enabled intrusion detection(HDL-MEID)technique for clustered IIoT *** HDL-MEID model mainly intends to organize the IIoT devices into clusters and enabled secure ***,the HDL-MEID technique designs a new chaotic mayfly optimization(CMFO)based clustering approach for the effective choice of the Cluster Heads(CH)and organize ***,equilibrium optimizer with hybrid convolutional neural network long short-term memory(HCNNLSTM)based classification model is derived to identify the existence of the intrusions in the IIoT *** experimental analysis is performed to highlight the enhanced outcomes of the HDL-MEID technique and the results were investigated under different *** experimental results highlight the supremacy of the proposed HDL-MEID technique over recent state-of-the-art techniques.

关键词： Industrial internet of things security intrusion detection classification deep learning

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Big Data Analytics with Artificial Intelligence Enabled Environmental Air Pollution Monitoring Framework

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Computers, Materials & Continua 2022年第11期73卷 3235-3250页

作者： Manar Ahmed Hamza Hadil Shaiba Radwa Marzouk Ahmad Alhindi Mashael M.Asiri Ishfaq Yaseen Abdelwahed Motwakel mohammed rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia&Research and InnovationSalla Holding LimitedMakkahSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil Asir62529Saudi Arabia

Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby highly populated countries like India. Due to increased population, thenumber of vehicles also continues to increase. Each vehicle has its individualemission rate;however, the issue arises when the emission rate crosses thestandard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop predictionapproaches to monitor and control pollution using real time data. With thedevelopment of the Internet of Things (IoT) and Big Data Analytics (BDA),there is a huge paradigm shift in how environmental data are employed forsustainable cities and societies, especially by applying intelligent *** this view, this study develops an optimal AI based air quality prediction andclassification (OAI-AQPC) model in big data environment. For handling bigdata from environmental monitoring, Hadoop MapReduce tool is *** addition, a predictive model is built using the hybridization of ARIMAand neural network (NN) called ARIMA-NN to predict the pollution *** improving the performance of the ARIMA-NN algorithm, the parametertuning process takes place using oppositional swallow swarm optimization(OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS)classifier is used to classify the air quality into pollutant and non-pollutant.A detailed experimental analysis is performed for highlighting the betterprediction performance of the proposed ARIMA-NN method. The obtainedoutcomes pointed out the enhanced outcomes of the proposed OAI-AQPCtechnique over the recent state of art techniques.

关键词： Sustainability environmental air quality predictive model pollution monitoring statistical models artificial intelligence

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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

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Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

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Computers, Materials & Continua 2022年第4期71卷 825-842页

作者： Mesfer Al Duhayyim Marwa Obayya Fahd N.Al-Wesabi Anwer Mustafa Hilal mohammed rizwanullah Majdy M.Eltahir Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia

With technological advancements in 6G and Internet of Things(IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellularnetworks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timelysolutions for real-time applications such as medicine, tracking, surveillance,etc. Energy efficiency, data collection, and route planning are crucial processesto improve the network communication. These processes are highly difficultowing to high mobility, presence of non-stationary links, dynamic topology,and energy-restricted UAVs. With this motivation, the current research paperpresents a novel Energy Aware Data Collection with Routing Planning for6G-enabled UAV communication (EADCRP-6G) technique. The goal of theproposed EADCRP-6G technique is to conduct energy-efficient cluster-baseddata collection and optimal route planning for 6G-enabled UAV ***-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) andorganize these clusters. Besides, Artificial Fish Swarm-based Route Planning(AFSRP) technique is applied to choose an optimum set of routes for UAVcommunication in 6G networks. In order to validated whether the proposedEADCRP-6G technique enhances the performance, a series of simulationswas performed and the outcomes were investigated under different *** experimental results showcase that the proposed model outperformed allother existing models under different evaluation parameters.

关键词： Unmanned aerial vehicle 6G networks artificial intelligence energy efficiency clustering route planning data collection metaheuristics

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Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

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Intelligent Automation & Soft Computing 2023年第6期36卷 3121-3137页

作者： Najm Alotaibi Badriyya B.Al-onazi Mohamed K.Nour Abdullah Mohamed Abdelwahed Motwakel Gouse Pasha mohammed Ishfaq Yaseen mohammed rizwanullah Prince Saud AlFaisal Institute for Diplomatic Studies Riyadh13369Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Opinion Mining(OM)studies in Arabic are limited though it is one of the most extensively-spoken languages *** the interest in OM studies in the Arabic language is growing among researchers,it needs a vast number of investigations due to the unique morphological principles of the *** OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic *** comparative OM studies in the English language are wide and ***,comparative OM studies in the Arabic language are yet to be established and are still in a nascent *** unique features of the Arabic language make it essential to expand the studies regarding the Arabic *** contains unique featuressuchasdiacritics,elongation,*** study proposes a Political Optimizer with Probabilistic Neural Network-based Comparative Opinion Mining(POPNN-COM)model for the Arabic *** proposed POPNN-COM model aims to recognize comparative and non-comparative texts in Arabic in the context of social ***,the POPNN-COM model involves different levels of data pre-processing to transform the input data into a useful ***,the pre-processed data is fed into the PNN model for classification and recognition of the data under different class *** last,the PO algorithm is employed for fine-tuning the parameters involved in this model to achieve enhanced *** proposed POPNN-COM model was experimentally validated using two standard datasets,and the outcomes established the promising performance of the proposed POPNN-COM method over other recent approaches.

关键词： Comparative opinion mining Arabic text social media parameter tuning machine learning political optimizer

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Deep Learning Enabled Computer Aided Diagnosis Model for Lung Cancer using Biomedical CT Images

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Computers, Materials & Continua 2022年第10期73卷 1437-1448页

作者： Mohammad Alamgeer Hanan Abdullah Mengash Radwa Marzouk Mohamed K Nour Anwer Mustafa Hilal Abdelwahed Motwakel Abu Sarwar Zamani mohammed rizwanullah Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Early detection of lung cancer can help for improving the survival rate of the *** imaging tools such as computed tomography(CT)image was utilized to the proper identification and positioning of lung *** recently developed deep learning(DL)models can be employed for the effectual identification and classification of *** article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image,named DLCADLC-BCT *** proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT *** proposed DLCADLC-BCT technique initially uses gray level co-occurrence matrix(GLCM)model for feature ***,long short term memory(LSTM)model was applied for classifying the existence of lung cancer in the CT ***,moth swarm optimization(MSO)algorithm is employed to optimally choose the hyperparameters of the LSTM model such as learning rate,batch size,and epoch *** demonstrating the improved classifier results of the DLCADLC-BCT approach,a set of simulations were executed on benchmark dataset and the outcomes exhibited the supremacy of the DLCADLC-BCT technique over the recent approaches.

关键词： Biomedical images lung cancer deep learning machine learning metaheuristics hyperparameter tuning

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Intelligent Cybersecurity Classification Using Chaos Game Optimization with Deep Learning Model

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Computer Systems Science & Engineering 2023年第4期45卷 971-983页

作者： Eatedal Alabdulkreem Saud S.Alotaibi Mohammad Alamgeer Radwa Marzouk Anwer Mustafa Hilal Abdelwahed Motwakel Abu Sarwar Zamani mohammed rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi 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 UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariJhnujhunuRajasthanIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Cyberattack detection has become an important research domain owing to increasing number of cybercrimes in recent *** Machine Learning(ML)and Deep Learning(DL)classification models are useful in effective identification and classification of *** addition,the involvement of hyper parameters in DL models has a significantly influence upon the overall performance of the classification *** this background,the current study develops Intelligent Cybersecurity Classification using Chaos Game Optimization with Deep Learning(ICC-CGODL)*** goal of the proposed ICC-CGODL model is to recognize and categorize different kinds of attacks made upon ***,ICC-CGODL model primarily performs min-max normalization process to normalize the data into uniform *** addition,Bidirectional Gated Recurrent Unit(BiGRU)model is utilized for detection and classification of ***,CGO algorithm is also exploited to adjust the hyper parameters involved in BiGRU model which is the novelty of current work.A wide-range of simulation analysis was conducted on benchmark dataset and the results obtained confirmed the significant performance of ICC-CGODL technique than the recent approaches.

关键词： Deep learning chaos game optimization cybersecurity chaos game optimization cyberattack

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