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Security Monitoring and management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

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computer Systems Science & Engineering 2024年第2期48卷 363-394页

作者： Nasser Alshammari Shumaila Shahzadi Saad Awadh Alanazi Shahid Naseem Muhammad Anwar Madallah Alruwaili Muhammad Rizwan Abid Omar Alruwaili Ahmed Alsayat Fahad Ahmad Department of computer Science College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72341Saudi Arabia Department of computer Sciences Kinnaird College forWomenLahorePunjab54700Pakistan Department of Information Sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan Department of computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72341Saudi Arabia computer Science Florida Polytechnic UniversityLakelandFlorida33805USA Delta3T LahorePunjab54700Pakistan Department of Basic Sciences Common First YearJouf UniversitySakaka72341Saudi Arabia

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies *** vulnerabilities try to modify services provided by Network Function Virtualization management and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end *** anomaly detector investigates these identified risks and provides secure network *** enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)*** anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation *** classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

关键词： Software defined network network function virtualization network function virtualization management and orchestration virtual infrastructure manager virtual network function Kubernetes Kubectl artificial intelligence machine learning

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Machine Learning Empowered Security and Privacy Architecture for IoT Networks with the Integration of Blockchain

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Intelligent Automation & Soft Computing 2024年第2期39卷 353-379页

作者： Sohaib Latif M.Saad Bin Ilyas Azhar Imran Hamad Ali Abosaq Abdulaziz Alzubaidi Vincent Karovic Jr. Department of computer Science The University of ChenabGujrat50700Pakistan Department of Creative Technologies Air UniversityIslamabad42000Pakistan computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran66244Saudi Arabia Department of computer Science Umm Alqura UniversityAlQunfudah28821Saudi Arabia Department of Information Systems Faculty of ManagementComenius University in BratislavaBratislava82005Slovakia

The Internet of Things(IoT)is growing rapidly and impacting almost every aspect of our lives,fromwearables and healthcare to security,traffic management,and fleet management *** has generated massive volumes of data and security,and data privacy risks are increasing with the advancement of technology and network *** access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF ***,conventional privacy preservation methods have high latency costs and overhead for resource-constrained *** machine learning approaches were also unable to detect denial-of-service(DoS)*** study introduced a novel decentralized and secure framework for blockchain *** avoid single-point OF failure,an accredited access control scheme is incorporated,combining blockchain with local peers to record each transaction and verify the signature to ***-based attribute-based cryptography is implemented to protect data storage privacy by generating threshold parameters,managing keys,and revoking users on the *** innovative contract-based DOS attack mitigation method is also incorporated to effectively validate devices with intelligent contracts as trusted or untrusted,preventing the server from becoming *** proposed framework effectively controls access,safeguards data privacy,and reduces the risk of *** results depict that the suggested framework outperforms the results in terms of accuracy,precision,sensitivity,recall,and F-measure at 96.9%,98.43%,98.8%,98.43%,and 98.4%,respectively.

关键词： Machine learning internet of things blockchain data privacy security Industry 4.0

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Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

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computers, Materials & Continua 2024年第3期78卷 3283-3301页

作者： Rafi Ullah Mohd Hilmi bin Hasan Sultan Daud Khan Mussadiq Abdul Rahim computer and Information Sciences Department Universiti Teknologi PETRONASBandar Seri Iskandar Perak32610Malaysia Department of computer Science National University of TechnologyIslamabad46000Pakistan Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and MineralsDammam31261Saudi Arabia

Medical imaging plays a key role within modern hospital management systems for diagnostic *** methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image ***,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy ***,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression *** response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking *** watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion *** the process of watermark embedding,motion vectors are selected based on their magnitude and phase *** embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the *** embedding of watermark bits is dependent on motion *** reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking *** reason for this is that the use of reversible watermarks may impede the claims of ownership and legal *** restoration of original data or images may call into question ownership or other legal *** peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image *** all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds *** results substantiate the efficacy of the proposed technique in preserving data integrity.

关键词： Block matching algorithm(BMA) compression full-search algorithm motion vectors ultrasound image sequence watermarking

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Towards sustainable adversarial training with successive perturbation generation

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Frontiers of Information Technology & Electronic Engineering 2024年第4期25卷 527-539页

作者： Wei LIN Lichuan LIAO College of computer Science and Mathematics Fujian University of TechnologyFuzhou 350118China College of Economics and management Xi’an University of TechnologyXi’an 710048China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou 350118China

Adversarial training with online-generated adversarial examples has achieved promising performance in defending adversarial attacks and improving robustness of convolutional neural network ***,most existing adversarial training methods are dedicated to finding strong adversarial examples for forcing the model to learn the adversarial data distribution,which inevitably imposes a large computational overhead and results in a decrease in the generalization performance on clean *** this paper,we show that progressively enhancing the adversarial strength of adversarial examples across training epochs can effectively improve the model robustness,and appropriate model shifting can preserve the generalization performance of models in conjunction with negligible computational *** this end,we propose a successive perturbation generation scheme for adversarial training(SPGAT),which progressively strengthens the adversarial examples by adding the perturbations on adversarial examples transferred from the previous epoch and shifts models across the epochs to improve the efficiency of adversarial *** proposed SPGAT is both efficient and effective;e.g.,the computation time of our method is 900 min as against the 4100 min duration observed in the case of standard adversarial training,and the performance boost is more than 7%and 3%in terms of adversarial accuracy and clean accuracy,*** extensively evaluate the SPGAT on various datasets,including small-scale MNIST,middle-scale CIFAR-10,and large-scale *** experimental results show that our method is more efficient while performing favorably against state-of-the-art methods.

关键词： Adversarial training Adversarial attack Stochastic weight average Machine learning Model generalization

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Design of a Novel Exoskeleton with Passive Magnetic Spring Self-locking and Spine Lateral Balancing

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Journal of Bionic Engineering 2024年第1期21卷 236-255页

作者： Jhon F.Rodríguez-León Betsy D.M.Chaparro-Rico Daniele Cafolla Francesco Lago Eduardo Castillo-Castañeda Giuseppe Carbone Centro de Investigacion en Ciencia Aplicada y Tecnologia Avanzada Unidad Querétaro MecatronicaInstituto Politécnico NacionalCerro Blanco 14176090 Colinas del CimatarioQuerétaroMexico Department of Mechanical Energy and Management EngineeringUniversity of CalabriaVia P.Bucci Cubo 46 C87036 RendeItaly Faculty of Science and Engineering Department of Computer ScienceSwansea UniversitySwanseaUK CESTER University of Cluj-NapocaCluj-NapocaRomania

This paper proposes a new upper-limb exoskeleton to reduce worker physical *** proposed design is based on a novel PRRRP(P-Prismatic;R-Revolute)kinematic chain with 5 passive Degrees of Freedom(DoF).Utilizing a magnetic spring,the proposed mechanism includes a specially designed locking mechanism that maintains any desired task *** proposed exoskeleton incorporates a balancing mechanism to alleviate discomfort and spinal torsional effects also helping in limb weight *** paper reports specific models and simulations to demonstrate the feasibility and effectiveness of the proposed *** experimental characterization is performed to validate the performance of the mechanism in terms of forces and physical strain during a specific application consisting of ceiling-surface drilling *** obtained results preliminarily validate the engineering feasibility and effectiveness of the proposed exoskeleton in the intended operation task thereby requiring the user to exert significantly less force than when not wearing it.

关键词： Assistive mechanism Exoskeleton Locking mechanism Bionic robot

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Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

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computer Modeling in Engineering & Sciences 2024年第8期140卷 1459-1483页

作者： Hongliang Zhang Yi Chen Yuteng Zhang Gongjie Xu School of management Science and Engineering Anhui University of TechnologyMa’anshan243032China Laboratory of Multidisciplinary management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of TechnologyMa’anshan243032China Department of Industrial Engineering School of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’an710072China

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing *** DFJSP research only considers machine constraints and ignores worker *** one critical factor of production,effective utilization of worker resources can increase ***,energy consumption is a growing concern due to the increasingly serious environmental ***,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this *** solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling *** further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are *** strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto *** effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 *** results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

关键词： Distributed flexible job shop scheduling problem dual resource constraints energy-saving scheduling multi-objective grey wolf optimizer Q-learning

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Efficient Ship:A Hybrid Deep Learning Framework for Ship Detection in the River

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工程与科学中的计算机建模(英文) 2024年第1期138卷 301-320页

作者： Huafeng Chen Junxing Xue Hanyun Wen Yurong Hu Yudong Zhang School of computer Engineering Jingchu University of TechnologyJingmen448000China School of computer Science Yangtze UniversityJingzhou434023China School of Computing and Mathematic Sciences University of LeicesterLeicesterLE17RHUK

Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore *** deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of *** solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this *** core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship *** also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model *** compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called *** show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.

关键词： Ship detection deep learning data augmentation object location object classification

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Fuzzy and IRLNC-based routing approach to improve data storage and system reliability in IoT

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Intelligent and Converged Networks 2024年第1期5卷 68-80页

作者： U.Indumathi A.R.Arunachalam the Department of computer Science and Engineering Dr.M.G.R.Educational and Research InstituteChennai 600095India

Internet of Things(IoT)based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper *** performing this entire process,there is a high possibility for data corruption in the mid of *** the other hand,the network performance is also affected due to various *** address these issues,an efficient algorithm that jointly offers improved data storage and reliable routing is ***,after the deployment of sensor nodes,the election of the storage node is achieved based on a fuzzy expert *** Random Linear Network Coding(IRLNC)is used to create an encoded *** encoded packet from the source and neighboring nodes is transmitted to the storage ***,to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector(DSDV)*** analysis of the proposed work is carried out by evaluating some of the statistical *** residual energy,packet delivery ratio,compression ratio and storage time achieved for the proposed work are 8.8%,0.92%,0.82%,and 69 *** on this analysis,it is revealed that better data storage system and system reliability is attained using this proposed work.

关键词： Internet of Things(IoT) data storage management fuzzy system improved random linear network coding energy utilization system reliability

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IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems

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computer Modeling in Engineering & Sciences 2024年第5期139卷 1487-1503页

作者： Dinesh Mavaluru Chettupally Anil Carie Ahmed I.Alutaibi Satish Anamalamudi Bayapa Reddy Narapureddy Murali Krishna Enduri Md Ezaz Ahmed School of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of computer Engineering SRM University APGunturIndia College of computer and Information Sciences Majmaah UniversityAl Majma’ahSaudi Arabia Department of Public Health College of Applied Medical SciencesKing Khalid UniversityAbhaSaudi Arabia

In this paper,we present a comprehensive system model for Industrial Internet of Things(IIoT)networks empowered by Non-Orthogonal Multiple Access(NOMA)and Mobile Edge Computing(MEC)*** network comprises essential components such as base stations,edge servers,and numerous IIoT devices characterized by limited energy and computing *** central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy *** system operates in discrete time slots and employs a quasi-static approach,with a specific focus on the complexities of task partitioning and the management of constrained resources within the IIoT *** study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation,particularly relevant in real-time industrial *** results indicate that our proposed algorithmsignificantly outperforms existing approaches,reducing queue backlog by 45.32% and 17.25% compared to SMRA and ACRA while achieving a 27.31% and 74.12% improvement in Qn ***,the algorithmeffectively balances complexity and network performance,as demonstratedwhen reducing the number of devices in each group(Ng)from 200 to 50,resulting in a 97.21% reduction in complexity with only a 7.35% increase in energy *** research offers a practical solution for optimizing IIoT networks in real-time industrial settings.

关键词： Internet of Things edge computing offloading NOMA

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A Multilayer Perceptron Artificial Neural Network Study of Fatal Road Traffic Crashes

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Journal of Data Analysis and Information Processing 2024年第3期12卷 419-431页

作者： Ed Pearson III Aschalew Kassu Louisa Tembo Oluwatodimu Adegoke Department of Electrical Engineering & computer Science Alabama A&M University Normal AL USA Department of Mechanical Civil Engineering & Construction Management Alabama A&M University Normal AL USA Department of Mathematics and computer Science Alabama State University Montgomery AL USA

This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.

关键词： Artificial Neural Network Multilayer Perceptron Fatal Crash Traffic Safety

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