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An Optimized Approach to Deep Learning for Botnet Detection and Classification for Cybersecurity in Internet of Things Environment

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Computers, Materials & Continua 2024年第8期80卷 2331-2349页

作者： Abdulrahman alzahrani Department of Computer Science and Engineering College of Computer Science and EngineeringUniversity of Hafr Al BatinP.O.Box 1803Hafr Al Batin39524Saudi Arabia

The recent development of the Internet of Things(IoTs)resulted in the growth of IoT-based DDoS *** detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected *** detection models evaluate transmission patterns,network traffic,and device behaviour to detect deviations from usual *** learning(ML)techniques detect patterns signalling botnet activity,namely sudden traffic increase,unusual command and control patterns,or irregular device *** addition,intrusion detection systems(IDSs)and signature-based techniques are applied to recognize known malware signatures related to *** ML and deep learning(DL)techniques have been developed to detect botnet attacks in IoT *** overcome security issues in an IoT environment,this article designs a gorilla troops optimizer with DL-enabled botnet attack detection and classification(GTODL-BADC)*** GTODL-BADC technique follows feature selection(FS)with optimal DL-based classification for accomplishing security in an IoT *** da.a.preprocessing,the min-max da.a.normalization approach is primarily *** GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature ***,the multi-head attention-based long short-term memory(MHA-LSTM)technique was applied for botnet ***,the tree seed algorithm(TSA)was used to select the optimum hyperpa.a.eter for the MHA-LSTM *** experimental validation of the GTODL-BADC technique can be tested on a benchmark *** simulation results highlighted that the GTODL-BADC technique demonstrates promising performance in the botnet detection process.

关键词： Botnet detection internet of things gorilla troops optimizer hyperpa.a.eter tuning intrusion detection system

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Evaluating the Efficacy of Latent Variables in Mitigating Da.a.Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1635-1654页

作者： Sha.a. alzahrani Hatim Alsuwat Emad Alsuwat Independent Researcher Specializing in Da.a.Security and Privacy Taif26571Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah24382Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif26571Saudi Arabia

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among ***,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming da.a.*** of the primary challenges with Bayesian networks is their vulnerability to adversarial da.a.poisoning attacks,wherein malicious da.a.is injected into the training da.a.et to negatively influence the Bayesian network models and impair their *** this research paper,we propose an efficient framework for detecting da.a.poisoning attacks a.a.nst Bayesian network structure learning *** framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over *** use our innovative methodology to tackle an important issue with da.a.poisoning assaults in the context of Bayesian *** regard to four different forms of da.a.poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC *** doing this,we explore the complexity of this area.a.d offer workablemethods for identifying and reducing these sneaky ***,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of da.a.poisoning are explored in this inquiry,which is of utmost *** results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of da.a.poisoning ***,our proposed latent-based framework proves to be sensitive in detecting malicious da.a.poisoning attacks in the context of stream da.a.

关键词： Bayesian networks da.a.poisoning attacks latent variables structure learning algorithms adversarial attacks

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A review of mechanism of action of outdoor exposure in preventing myopia incidence and progression

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International Journal of Ophthalmology(English edition) 2024年第6期17卷 1144-1155页

作者： Chikezie Grand Ihesiulor Khaled alzahrani Hema Rhadhakrishnan Division of Pharmacy&Optometry School of Health SciencesFaculty of BiologyMedicine and HealthThe University of ManchesterManchester M139PLUnited Kingdom Department of Ophthalmology King Fahad Armed Forces HospitalJeddah 23311Saudi Arabia

Various studies have suggested several environmental,pharmacological,medical,and optical interventions and some are in use but their efficacy in myopia control may be transient,and the cellular,molecular,and biochemical mechanisms involved *** exposure is currently regarded as an effective and enduring strategy in the control of myopia development and ***,the mechanism behind the effect of outdoor exposure and its association with genetic predisposition and other relatively more significant environmental factors on myopia is still a *** review focuses on survey-based and intervention-based studies carried out to propose a mechanism that accounts for myopia development and important for its control.

关键词： outdoor exposure daylight exposure myopia control myopia

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A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan Kumar Neeraj Kumar Bander alzahrani a.a. Almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing da.a.locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time a.a.ysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive da.a.close to the source,reducing the chances of da.a.exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry da.a.et *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

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A Lightweight Intrusion Detection System Using Convolutional Neural Network and Long Short-Term Memory in Fog Computing

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Computers, Materials & Continua 2024年第9期80卷 4703-4728页

作者： Ha.a.en alzahrani Tarek Sheltami Abdulaziz Barnawi Muhammad Imam Ansar Yaser Computer Engineering Department Interdisciplinary Research Center of Smart Mobility and LogisticsKing Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Computer Engineering Department Interdisciplinary Research Center for Intelligent Secure SystemsKing Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Transportation Research Institute(IMOB) Hasselt UniversityHasselt3500Belgium

The Internet of Things(IoT)links various devices to digital services and significantly improves the quality of our ***,as IoT connectivity is growing rapidly,so do the risks of network vulnerabilities and *** interesting Intrusion Detection Systems(IDSs)are presented based on machine learning(ML)techniques to overcome this *** the resource limitations of fog computing environments,a lightweight IDS is *** paper introduces a hybrid deep learning(DL)method that combines convolutional neural networks(CNN)and long short-term memory(LSTM)to build an energy-a.a.e,anomaly-based *** test this system on a recent da.a.et,focusing on reducing overhead while maintaining high accuracy and a low false a.a.m *** compare CICIoT2023,KDD-99 and NSL-KDD da.a.ets to evaluate the performance of the proposed IDS model based on key metrics,including latency,energy consumption,false a.a.m rate and detection rate *** findings show an accuracy rate over 92%and a false a.a.m rate below 0.38%.These results demonstrate that our system provides strong security without excessive resource *** practicality of deploying IDS with limited resources is demonstrated by the successful implementation of IDS functionality on a Raspberry Pi acting as a Fog *** proposed lightweight model,with a maximum power consumption of 6.12 W,demonstrates its potential to operate effectively on energy-limited devices such as low-power fog nodes or edge *** prioritize energy efficiency whilemaintaining high accuracy,distinguishing our scheme fromexisting *** experiments demonstrate a significant reduction in false positives,ensuring accurate identification of genuine security threats while minimizing unnecessary alerts.

关键词： Intrusion detection fog computing CNN LSTM energy consumption

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Implications of electrode modifications in aqueous organic redox flow batteries

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Journal of Energy Chemistry 2024年第8期95卷 499-510,I0011页

作者： Zahid Manzoor Bhat Mohammad Furquan Muhammad Aurang Zeb Gul Sial Umair a.a. Atif Saeed alzahrani Mohammad Qa.a. Interdisciplinary Research Center for Hydrogen Technologies and Carbon Ma.a.ement(IRC-HTCM) King Fahd University of Petroleum&Minerals(KFUPM)Dhahran 31261Saudi Arabia Department of Materials Science and Engineering King Fahd University of Petroleum and MineralsDhahran 31261Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems(IRC-SES) King Fahd University of Petroleum and MineralsDhahran 31261Saudi Arabia

Aqueous organic redox flow batteries(RFBs)exhibit favorable cha.a.teristics,such as tunability,multielectron transfer ca.a.ility,and stability of the redox active molecules utilized as anolytes and catholytes,making them very viable contenders for large-scale grid storage *** attention has been paid on the development of efficient redox-active molecules and their performance optimization through chemical substitutions at various places on the backbone as part of the pursuit for high-performance *** the fact that electrodes are vital to optimal performance,they have not garnered significant *** research has been conducted on the effects of electrode modifications to improve the performance of *** primary emphasis has been given on the impact of electrode engineering to augment the efficiency of aqueous organic *** overview of electron transfer at the electrode-electrolyte interface is *** implications of electrode modification on the performance of redox flow batteries,with a particular focus on the anodic and cathodic half-cells sepa.a.ely,are then *** each section,significant discrepancies surrounding the effects of electrode engineering are thoroughly examined and ***,we have presented a comprehensive assessment along with our perspectives on the future trajectory.

关键词： Redox flow batteries Electrode modification Organic redox molecules Outer sphere and inner sphere

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A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2497-2517页

作者： Nasser a.a.wan Ahmed I.Taloba.a.r Abozeid Ahmed Ibrahim alzahrani Ali H.Al-Ba.a.ti Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Information System Department Faculty of Computers and InformationAssiut UniversityAssiutEgypt Mathematics Department Faculty of ScienceAl-Azhar UniversityCairoEgypt Cyber Technology Institute(CTI) De Montfort UniversityLeicesterUK

An illness known as pneumonia causes inflammation in the *** there is so much information a.a.lable fromvarious X-ray images,diagnosing pneumonia has typically proven *** improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been *** date,several methods have been employed to identify *** Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and ***,these methods are complex,inefficient,and imprecise to a.a.yze a big number of *** this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is *** proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and *** Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)*** features are then selected from the da.a.et using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of *** results on various da.a.ets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification *** proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.

关键词： African buffalo optimization convolutional neural network pneumonia X-ray

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The User a.a.ysis of a.a.on Using Artificial Intelligence at Customer Churn

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Journal of Da.a.a.a.ysis and Information Processing 2024年第1期12卷 40-48页

作者： Mohammed Ali alzahrani Information Technology Department Computing and ICT Faculty Taif University Taif Saudi Arabia

Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based a.a.ysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full a.a.ysis of the da.a.set. Da.a.set is taken from the Kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based a.a.ysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific a.a.ysis helps e-commerce business for real time growth in their specific products focusing more sales and to a.a.yze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based a.a.ysis on fine food reviews, a.a.on at customer churn Support Vector Machine performed better as in overall comparison.

关键词： Customer Churn Machine Learning a.a.on Fine Food Reviews Da.a.Science Artificial Intelligence

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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Computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwa.a. a.a.eb a.a.d Algarni Hamdan A.alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object da.a.ets like MSRC-v2 and PASCAL-VOC12,have been *** a.a.yzing the experimental results,it was found that the PASCAL-VOC12 da.a.et achieved an accuracy rate of 95%,while the MSRC-v2 da.a.et achieved an accuracy of 89%.The evaluation performed on these diverse da.a.ets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

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Computer Systems Science & Engineering 2024年第2期48卷 511-528页

作者： a.a. Bilal Azhar Imran Talha Imtiaz Baig Xiaowen Liu Haixia Long Abdulkareem alzahrani Muhammad Shafiq College of Information Science and Technology Hainan Normal UniversityHaikou571158China Department of Creative Technologies Air UniversityIslamabadPakistan School of Life Science and Technology University of Electronic Science and Technology of China UESTCChengduChina School of Science and Technology University of Management and Technology UMTLahorePunjabPakistan Faculty of Computer Science&Information Technology Al Baha UniversityAl BahaSaudi Arabia School of Information Engineering Qujing Normal UniversityQujingChina

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness ***,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to *** paper proposes a novel VTDR detection and classification model that combines different models through majority *** proposed methodology involves preprocessing,da.a.augmentation,feature extraction,and classification *** use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for *** tested our model on the IDRiD da.a.et and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,*** proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

关键词： Diabetic retinopathy(DR) fundus images(FIs) support vector machine(SVM) medical image a.a.ysis convolutional neural networks(CNN) singular value decomposition(SVD) classification

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