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Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

Computer Systems Science & Engineering 2023年第9期46卷 3121-3139页

作者： Khaled m.Alalayah Fatma S.Alrayes Jaber S.Alzahrani Khadija m.Alaidarous Ibrahim M.Alwayle Heba mohsen ibrahim Abdulrab Ahmed mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial transformation.The Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether.In industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network security.Though various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of networks.This is because conventional machine Learning(mL)approaches are inadequate and insufficient to address the demands of dynamic IIoT networks.Further,the existing Deep Learning(DL)can be employed to identify anonymous intrusions.Therefore,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT environment.The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format.The HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of dimensionality.moreover,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the network.Finally,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model.The proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： Industrial IoT deep learning network security intrusion detection system attribute selection smart factory

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Learning-Based Artificial Algae Algorithm with Optimal machine Learning Enabled malware Detection

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Computer Systems Science & Engineering 2023年第9期46卷 3103-3119页

作者： Khaled m.Alalayah Fatma S.Alrayes mohamed K.Nour Khadija m.Alaidarous Ibrahim M.Alwayle Heba mohsen ibrahim Abdulrab Ahmed mesfer Al Duhayyim Department of Computer Science College of Science and ArtsNajran UniversitySharurahSaudi 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 Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and cybercrime.Such malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,etc.Ransomware is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of money.To prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with machine Learning(mL)algorithm.This study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal machine Learning Enabled malware Detection(LBAAA-OmLmD)approach in Computer Networks.The presented LBAAA-OmLmDmodelmainly aims to detect and classify the existence of ransomware and goodware in the network.To accomplish this,the LBAAA-OmLmD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality problems.Besides,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is applied.The FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier results.The experimental validation of the LBAAA-OmLmD model is tested using a benchmark dataset,and the outcomes are inspected in distinct measures.The comprehensive comparative examination demonstrated the betterment of the LBAAAOmLmD model over recent algorithms.

关键词： Computer networks machine learning security malware detection feature selection ransomware

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Parameter Tuned machine Learning Based Emotion Recognition on Arabic Twitter Data

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Computer Systems Science & Engineering 2023年第9期46卷 3423-3438页

作者： Ibrahim M.Alwayle Badriyya B.Al-onazi Jaber S.Alzahrani Khaled m.Alalayah Khadija m.Alaidarous ibrahim Abdulrab Ahmed mahmoud Othman Abdelwahed motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajran55461Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityNajran24211Saudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi 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

Arabic is one of the most spoken languages across the globe.However,there are fewer studies concerning Sentiment Analysis(SA)in Arabic.In recent years,the detected sentiments and emotions expressed in tweets have received significant interest.The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language.Two common models are available:machine Learning and lexicon-based approaches to address emotion classification problems.With this motivation,the current research article develops a Teaching and Learning Optimization with machine Learning Based Emotion Recognition and Classification(TLBOmL-ERC)model for Sentiment Analysis on tweets made in the Arabic language.The presented TLBOmL-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets.To attain this,the proposed TLBOmLERC model initially carries out data pre-processing and a Continuous Bag Of Words(CBOW)-based word embedding process.In addition,Denoising Autoencoder(DAE)model is also exploited to categorise different emotions expressed in Arabic tweets.To improve the efficacy of the DAE model,the Teaching and Learning-based Optimization(TLBO)algorithm is utilized to optimize the parameters.The proposed TLBOmL-ERC method was experimentally validated with the help of an Arabic tweets dataset.The obtained results show the promising performance of the proposed TLBOmL-ERC model on Arabic emotion classification.

关键词： Arabic language Twitter data machine learning teaching and learning-based optimization sentiment analysis emotion classification

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Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

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Intelligent Automation & Soft Computing 2023年第11期38卷 153-168页

作者： Ibrahim M.Alwayle Hala J.Alshahrani Saud S.Alotaibi Khaled m.Alalayah Amira Sayed A.Aziz Khadija m.Alaidarous ibrahim Abdulrab Ahmed manar Ahmed Hamza Department of Computer Science College of Science and ArtsSharurahNajran UniversitySharurahSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Digital media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the summary.This study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization efficiency.This phase shows the novelty of the current study.To validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was conducted.The comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.

关键词： Text summarization deep learning denoising deep neural networks hyperparameter tuning Arabic language

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Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

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Computer Systems Science & Engineering 2023年第9期46卷 2947-2961页

作者： ibrahimm.alwayle Badriyya B.Al-onazi mohamed K.Nour Khaled m.Alalayah Khadija m.Alaidarous ibrahim Abdulrab Ahmed Amal S.mehanna Abdelwahed motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi 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 UniversitySaudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Digital media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Online reviews regarding purchasing services or products offered are the main source of users’opinions.To gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or services.This practice is called review spamming.During the last few years,various techniques have been recommended to solve the problem of spam reviews.Previous spam detection study focuses on English reviews,with a lesser interest in other languages.Spam review detection in Arabic online sources is an innovative topic despite the vast amount of data produced.Thus,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion Text.The presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and truthful.Initially,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible format.Next,unigram and bigram feature extractors are utilized.The SGRU model is employed in this study to identify and classify Arabic spam reviews.Since the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU model.The experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC dataset.An extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.

关键词： Arabic text spam reviews machine learning deep learning white shark optimizer

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An efficient nano-adsorbent via surfactants/dual surfactants assisted ultrasonic co-precipitation method for sono-removal of monoazo and diazo anionic dyes

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Chinese Journal of Chemical Engineering 2021年第12期34卷 225-236页

作者： marwa m.ibrahim Department of Chemistry Faculty of EducationAin Shams UniversityRoxy 11711CairoEgypt

To preserve the environment for civilization,we should remove the pollutants like toxic dyes by friendly and cost efficacious method.In this study,the effect of surfactants or mixed surfactants on physicochemical,optical and adsorption properties of ternary mixed oxide CeO_(2)-ZrO_(2)-Al_(2) O_3(CZA) are investigated.The ternary mixed oxide CZA was prepared by surfactants or mixed surfactants assisted ultrasonic coprecipitation method.The physicochemical and optical properties are estimated by different techniques like XRD,TEm,EDX,FTIR,S_(BET) and UV-Vis/DR.The CZA_T and CZA_C have hybrid shapes and high surface area.The adsorption properties of ternary mixed oxides adsorbents were characterized by sono-removing anionic dyes such as Congo red(CR) and Remazol red RB-133(RR).The different factors like contact time,different dye concentrations and temperatures also studied.The kinetics and isotherms applications showed that,the adsorption process was followed pseudo second order kinetics and the Freundlich isotherm model.Also,the adsorption is spontaneous and endothermic process through the thermodynamic study.Finally,the results showed that the ternary mixed oxide nano-adsorbent(CeO_(2)-ZrO_(2)-Al_(2) O_3) is promising and functional materials for anionic dye sweep from wastewater.

关键词： CeO_(2)-ZrO_(2)-Al_(2)O_3 Surfactants Ultrasonic co-precipitation method Optical properties Wastewater Adsorption

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约旦冠心病患者心理韧性的调查研究

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International Journal of Nursing Sciences 2022年第1期9卷 86-91页

作者： Nahla m Al Ali ibrahim S Al Ramamneh Department of Community and mental Health Nursing Jordan University of Science&Technology/Faculty of NursingJordan

目的调查约旦冠心病患者心理韧性现状及相关因素。方法采用横断面调查研究,便利选取在约旦安曼市2所公立医院心内科门诊就诊的134例患者,并采用心理韧性量表(The Connor-Davidson Resilience Scale,CD-RISC)对其进行面对面调查。采用man... 详细信息

目的调查约旦冠心病患者心理韧性现状及相关因素。方法采用横断面调查研究,便利选取在约旦安曼市2所公立医院心内科门诊就诊的134例患者,并采用心理韧性量表(The Connor-Davidson Resilience Scale,CD-RISC)对其进行面对面调查。采用mann-Whitney U检验、Kruskal Wallis检验对数据进行分析。结果冠心病患者心理韧性得分为68.00(60.75,74.00),属于中等水平。没有心脏病手术史的患者心理韧性总得分[69.50(63.25,75.00)比65.50(58.00,72.00)]和“个人能力、高标准、坚韧维度”得分[22.00(18.50,26.00)比21.00(15.75,23.00)]均高于有心脏病手术史患者(P<0.05)。在职的患者“积极接受改变并确保关系维度”得分高于已经退休的患者[15.00(14.00,16.00)比14.00(12.00,15.00),P<0.05]。其他不同人口学资料患者的心理韧性及维度得分比较,差异均无统计学意义。结论应将冠心病患者的心理韧性水平及其相关因素纳入综合护理计划,以提高患者的生活质量,加强有效的应对策略,改善身心健康,并进一步预防并发症的发生。

关键词：冠心病约旦护理患者韧性,心理

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Ballistic resistance and energy dissipation of woven-fabric composite targets:Insights on the effects of projectile shape and obliquity angle

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Defence Technology（防务技术） 2023年第3期21卷 14-32页

作者： ibrahim Goda UniversitéBourgogne Franche-Comté FEMTO-ST InstituteDépartement Mécanique Appliquée24 Rue de L’Épitaphe25000BesançonFrance

This study aims at investigating the ballistic resistance and energy absorption in woven E-glass composite panels,considering different projectile nose shapes and oblique incidence angles.To that scope,three-dimensional finite element(FE)models of both projectiles and the laminated target are developed and numerical investigations are carried out using Abaqus Explicit solver.The composite damage model’s constitutive law encompasses nonlinear material response,material properties degradation,progressive failure,and an element deletion strategy.The cohesive surface technique is used to represent the interface between two adjacent plies in the laminate,and the traction-separation law is used to characterize the behaviors of interlaminar degradation and failure.material responses attributable to fiber rupture,matrix cracking,and plasticity caused by micro-matrix cracking due to shear loading are taken into account with suitable damage evolution laws.The computational framework is first validated against the experimental results reported in the literature by performing ballistic impact tests on the target laminate with conical,hemispherical and blunt-ended projectile,and the numerical results showed a good comparison in terms of residual velocity.Subsequently the framework is explored in simulating more complex failure mechanisms,with particular emphasis on the influence of the impact angle of obliquity,a parameter that is not usually analyzed in the literature.In that regard,the effects of normal and oblique impact on the damage morphologies and ballistic behavior of the fabric composite target in terms of energy absorption,impact contact force,and projectile residual velocity are conducted and analyzed,comparatively.The findings showed that the ballistic impact behavior of target composite is substantially influenced by projectile nose shape and incidence angle obliquity.

关键词： resistance cracking composite

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COVID-19 and Unemployment: A Novel Bi-Level Optimal Control model

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Computers, materials & Continua 2021年第4期67卷 1153-1167页

作者： ibrahim m.Hezam Department of Statistics and Operations Research College of SciencesKing Saud UniversityRiyadhSaudi Arabia Department of mathematics Ibb UniversityIbbYemen

Since COVID-19 was declared as a pandemic in march 2020,the world’s major preoccupation has been to curb it while preserving the economy and reducing unemployment.This paper uses a novel Bi-Level Dynamic Optimal Control model(BLDOC)to coordinate control between COVID-19 and unemployment.The COVID-19 model is the upper level while the unemployment model is the lower level of the bi-level dynamic optimal control model.The BLDOC model’s main objectives are to minimize the number of individuals infected with COVID-19 and to minimize the unemployed individuals,and at the same time minimizing the cost of the containment strategies.We use the modified approximation Karush–Kuhn–Tucker(KKT)conditions with the Hamiltonian function to handle the bi-level dynamic optimal control model.We consider three control variables:The first control variable relates to government measures to curb the COVID-19 pandemic,i.e.,quarantine,social distancing,and personal protection;and the other two control variables relate to government interventions to reduce the unemployment rate,i.e.,employment,making individuals qualified,creating new jobs reviving the economy,reducing taxes.We investigate four different cases to verify the effect of control variables.Our results indicate that rather than focusing exclusively on only one problem,we need a balanced trade-off between controlling each.

关键词： Bi-level optimal control COVID-19 Hamiltonian function Karush-Kuhn-Tucker unemployment

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New Improved Ranked Set Sampling Designs with an Application to Real Data

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Computers, materials & Continua 2021年第5期67卷 1503-1522页

作者： Amer ibrahim Al-Omari ibrahim m.Almanjahie Department of mathematics Faculty of ScienceAl al-Bayt UniversityMafraqJordan Department of mathematics College of ScienceKing Khalid UniversityAbha62529Saudi Arabia Statistical Research and Studies Support Unit King Khalid UniversityAbha62529Saudi Arabia

This article proposes two new Ranked Set Sampling(RSS)designs for estimating the population parameters:Simple Z Ranked Set Sampling(SZRSS)and Generalized Z Ranked Set Sampling(GZRSS).These designs provide unbiased estimators for the mean of symmetric distributions.It is shown that for non-uniform symmetric distributions,the estimators of the mean under the suggested designs are more efcient than those obtained by RSS,Simple Random Sampling(SRS),extreme RSS and truncation based RSS designs.Also,the proposed RSS schemes outperform other RSS schemes and provide more efcient estimates than their competitors under imperfect rankings.The suggested mean estimators under perfect and imperfect rankings are more efcient than the linear regression estimator under SRS.Our proposed RSS designs are also extended to cover the estimation of the population median.Real data is used to examine wthe usefulness and efciency of our estimators.

关键词： Ranked set sampling unbiased estimator simple random sampling mean squared error efciency imperfect ranking

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