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Optimal Deep Learning-based Cyberattack Detection and Classification Technique on Social Networks

Computers, Materials & Continua 2022年第7期72卷 907-923页

作者： Amani Abdulrahman Albraikan Siwar Ben Haj Hassine Suliman Mohamed Fati Fahd NAl-Wesabi Anwer Mustafa Hilal Abdelwahed Motwakel Manar Ahmed Hamza Mesfer Al Duhayyim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi 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 Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Cyberbullying(CB)is a distressing online behavior that disturbs mental health *** studies have employed statistical and Machine Learning(ML)techniques for CB *** this motivation,the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification(ODL-CDC)technique for CB detection in social *** proposed ODL-CDC technique involves different processes such as pre-processing,prediction,and hyperparameter *** addition,GloVe approach is employed in the generation of word ***,the pre-processed data is fed into BidirectionalGated Recurrent Neural Network(BiGRNN)model for ***,hyperparameter tuning of BiGRNN model is carried out with the help of Search and Rescue Optimization(SRO)*** order to validate the improved classification performance of ODL-CDC technique,a comprehensive experimental analysis was carried out upon benchmark dataset and the results were inspected under varying aspects.A detailed comparative study portrayed the superiority of the proposed ODL-CDC technique over recent techniques,in terms of performance,with the maximum accuracy of 92.45%.

关键词： Cybersecurity cyberbullying social networks parameter tuning deep learning metaheuristics

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

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Computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess 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 Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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Bird Swarm Algorithm with Fuzzy Min-Max Neural Network for Financial Crisis Prediction

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Computers, Materials & Continua 2022年第10期73卷 1541-1555页

作者： K.Pradeep Mohan Kumar S.Dhanasekaran I.S.Hephzi Punithavathi P.Duraipandy Ashit Kumar Dutta Irina V.Pustokhina Denis A.Pustokhin Department of Computing Technologies Associate ProfessorSRM Institute of Science and TechnologyKattankulathur603203India Department of Information Technology Kalasalingam Academy of Research and Education626126India Department of Computer Science and Engineering Sphoorthy Engineering CollegeHyderabadTelangana501510India Department of Electrical and Electronics Engineering J B Institute of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Financial crisis prediction(FCP)models are used for predicting or forecasting the financial status of a company or financial *** is considered a challenging issue in the financial *** and machine learning(ML)models can be employed for the design of accurate FCP *** numerous works have existed in the literature,it is needed to design effective FCP models adaptable to different *** study designs a new bird swarm algorithm(BSA)with fuzzy min-max neural network(FMM-NN)model,named BSA-FMMNN for *** major intention of the BSA-FMMNN model is to determine the financial status of a firm or *** presented BSA-FMMNN model primarily undergoes minmax normalization to transform the data into uniformity ***,k-medoid clustering approach is employed for the outlier removal ***,the classification process is carried out using the FMMNN model,and the parameters involved in it are tuned by the use of *** utilization of proficient parameter selection process using BSA demonstrate the novelty of the *** experimental result analysis of the BSA-FMMNN model is validated using benchmark dataset and the comparative outcomes highlighted the supremacy of the BSA-FMMNN model over the recent approaches.

关键词： Financial crisis predictive model machine learning outlier removal clustering metaheuristics

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Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

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Computer Systems Science & Engineering 2023年第4期45卷 701-714页

作者： Helen Josephine V L Ramchand Vedaiyan V.M.Arul Xavier Joy Winston J A.Jegatheesan D.Lakshmi Joshua Samuel Raj Business Analytics School of Business and ManagementChrist UniversityBangalore560029India Department of Computer Science and IT School of SciencesJain Deemed to be UniversityBangalore560027India Department of Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatore641114India College of Computer Studies University of Technology Bahrain18041Bahrain Institute of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Computer Science and Engineering Panimalar Institute of TechnologyChennai600123India Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India

The Internet of Things(IoT)technologies has gained significant interest in the design of smart grids(SGs).The increasing amount of distributed generations,maturity of existing grid infrastructures,and demand network transformation have received maximum *** essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further *** dynamic electrical energy stored model using Electric Vehicles(EVs)is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the *** paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder(HBFOA-SAE)model for IoT Enabled energy *** proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge(SOC)values in the IoT based energy *** accomplish this,the SAE technique was executed to proper determination of the SOC values in the energy ***,for improving the performance of the SOC estimation process,the HBFOA is *** addition,the HBFOA technique is derived by the integration of the hill climbing(HC)concepts with the BFOA to improve the overall *** ensuring better outcomes for the HBFOA-SAE model,a comprehensive set of simulations were performed and the outcomes are inspected under several *** experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.

关键词： Internet of things energy systems state of charge estimation machine learning deep learning metaheuristics

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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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Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System

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Computers, Materials & Continua 2023年第2期74卷 4435-4451页

作者： Radwa Marzouk Eatedal Alabdulkreem Mohamed KNour Mesfer Al Duhayyim Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess 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 Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi 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

The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart *** plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so *** an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be *** aim of the image captioning process is to generate a caption for *** next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically *** this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the *** current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input *** attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding ***,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)*** model represents the input data appropriately by inserting it into a predefined length ***,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the *** application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models *** proposed NLPODL-IICS model was experimentally validated with the help of two benchmark *** comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.

关键词： Natural language processing information retrieval image captioning deep learning metaheuristics

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Vision Based Real Time Monitoring System for Elderly Fall Event Detection Using Deep Learning

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Computer Systems Science & Engineering 2022年第7期42卷 87-103页

作者： G.Anitha S.Baghavathi Priya Department of Information Technology Rajalakshmi Engineering CollegeChennai602105India Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennai602105India

Human fall detection plays a vital part in the design of sensor based alarming system,aid physical therapists not only to lessen after fall effect and also to save human *** and timely identiﬁcation can offer quick medical ser-vices to the injured people and prevent from serious *** vision-based approaches have been developed by the placement of cameras in diverse everyday *** present times,deep learning(DL)models par-ticularly convolutional neural networks(CNNs)have gained much importance in the fall detection *** this motivation,this paper presents a new vision based elderly fall event detection using deep learning(VEFED-DL)*** proposed VEFED-DL model involves different stages of operations namely pre-processing,feature extraction,classiﬁcation,and parameter ***-ily,the digital video camera is used to capture the RGB color images and the video is extracted into a set of *** improving the image quality and elim-inate noise,the frames are processed in three levels namely resizing,augmenta-tion,and min–max based ***,MobileNet model is applied as a feature extractor to derive the spatial features that exist in the preprocessed *** addition,the extracted spatial features are then fed into the gated recur-rent unit(GRU)to extract the temporal dependencies of the human ***,a group teaching optimization algorithm(GTOA)with stacked autoenco-der(SAE)is used as a binary classiﬁcation model to determine the existence of fall or non-fall *** GTOA is employed for the parameter optimization of the SAE model in such a way that the detection performance can be *** order to assess the fall detection performance of the presented VEFED-DL model,a set of simulations take place on the UR fall detection dataset and multi-ple cameras fall *** experimental outcomes highlighted the superior per-formance of the presented method over the recent methods.

关键词： Computer vision elderly people fall detection deep learning metaheuristics object detection parameter optimization

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Optimal Machine Learning Enabled Intrusion Detection in Cyber-Physical System Environment

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Computers, Materials & Continua 2022年第9期72卷 4691-4707页

作者： Bassam A.Y.Alqaralleh Fahad Aldhaban Esam A.AlQarallehs Ahmad H.Al-Omari MIS Department College of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia School of Engineering Princess Sumaya University for TechnologyAmman11941Jordan Faculty of Science Computer Science DepartmentNorthern Border UniversityArar91431Saudi Arabia

Cyber-attacks on cyber-physical systems(CPSs)resulted to sensing and actuation misbehavior,severe damage to physical object,and safety *** learning(ML)models have been presented to hinder cyberattacks on the CPS environment;however,the non-existence of labelled data from new attacks makes their detection quite *** Detection System(IDS)is a commonly utilized to detect and classify the existence of intrusions in the CPS environment,which acts as an important part in secure CPS *** developments in deep learning(DL)and explainable artificial intelligence(XAI)stimulate new IDSs to manage cyberattacks with minimum complexity and high *** this aspect,this paper presents an XAI based IDS using feature selection with Dirichlet Variational Autoencoder(XAIIDS-FSDVAE)model for *** proposed model encompasses the design of coyote optimization algorithm(COA)based feature selection(FS)model is derived to select an optimal subset of ***,an intelligent Dirichlet Variational Autoencoder(DVAE)technique is employed for the anomaly detection process in the CPS ***,the parameter optimization of the DVAE takes place using a manta ray foraging optimization(MRFO)model to tune the parameter of the *** order to determine the enhanced intrusion detection efficiency of the XAIIDS-FSDVAE technique,a wide range of simulations take place using the benchmark *** experimental results reported the better performance of the XAIIDSFSDVAE technique over the recent methods in terms of several evaluation parameters.

关键词： Cyber-physical systems explainable artificial intelligence deep learning security intrusion detection metaheuristics

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Energy Aware Metaheuristic Optimization with Location Aided Routing Protocol for MANET

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Computers, Materials & Continua 2022年第4期71卷 1567-1580页

作者： E.Ahila Devi K.C.Ramya K.Sathesh Kumar Sultan Ahmad Seifedine Kadry Hyung Ju Park Byeong-Gwon Kang Department of Instrumentation and Control Engineering St.Joseph’s College of EngineeringChennai600119India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

A mobile ad hoc network(MANET)involves a group of wireless mobile nodes which create an impermanent network with no central authority and *** nodes in the MANET are highly mobile and it results in adequate network topology,link loss,and increase the re-initialization of the route discovery *** planning in MANET is a multi-hop communication process due to the restricted transmission range of the *** aided routing(LAR)is one of the effective routing protocols in MANET which suffers from the issue of high energy *** few research works have focused on resolving energy consumption problem in LAR,energy efficiency still remains a major design *** this aspect,this study introduces an energy aware metaheuristic optimization with LAR(EAMO-LAR)protocol for *** EAMO-LAR protocol makes use of manta ray foraging optimization algorithm(MRFO)to help the searching process for the individual solution to be passed to the LAR *** fitness value of the created solutions is determined next to pass the solutions to the objective *** MRFO algorithm is incorporated into the LAR protocol in the EAMO-LAR protocol to reduce the desired energy *** ensure the improved routing efficiency of the proposed EAMO-LAR protocol,a series of simulations take *** resultant experimental values pointed out the supreme outcome of the EAMO-LAR protocol over the recently compared *** resultant values demonstrated that the EAMO-LAR protocol has accomplished effectual results over the other existing techniques.

关键词： MANET routing lar protocol energy efficiency mrfo algorithm metaheuristics

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A Novel Binary Emperor Penguin Optimizer for Feature Selection Tasks

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Computers, Materials & Continua 2022年第3期70卷 6239-6255页

作者： Minakshi Kalra Vijay Kumar Manjit Kaur Sahar Ahmed Idris Saban Oturk Hammam Alshazly Computer Science Department Government College BahadurgarhBahadurgarh124507India CSE Department National Institute of Technology Hamirpur177005India School of Engineering and Applied Sciences Bennett UniversityGreater Noida201310India College of Industrial Engineering King Khalid UniversityAbhaSaudi Arabia Department of Electrical and Electronics Engineering Amasya UniversityAmasyaTurkey Faculty of Computers and Information South Valley UniversityQena83523Egypt

Nowadays,due to the increase in information resources,the number of parameters and complexity of feature vectors *** offermore practical solutions instead of exact solutions for the solution of this *** Emperor PenguinOptimizer(EPO)is one of the highest performing meta-heuristic algorithms of recent times that imposed the gathering behavior of emperor *** shows the superiority of its performance over a wide range of optimization problems thanks to its equal chance to each penguin and its fast convergence *** traditional EPO overcomes the optimization problems in continuous search space,many problems today shift to the binary search ***,in this study,using the power of traditional EPO,binary EPO(BEPO)is presented for the effective solution of binary-nature *** algorithm uses binary search space instead of searching solutions like conventional EPO algorithm in continuous search *** this purpose,the sigmoidal functions are preferred in determining the emperor *** addition,the boundaries of the search space remain constant by choosing binary ***’s performance is evaluated over twenty-nine benchmarking *** evaluations are made to reveal the superiority of the BEPO *** addition,the performance of the BEPO algorithm was evaluated for the binary feature selection *** experimental results reveal that the BEPO algorithm outperforms the existing binary meta-heuristic algorithms in both tasks.

关键词： metaheuristics optimization algorithms emperor penguin optimizer intensification diversification feature selection

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