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IEEE International Conference on system, Computation, Automation and Networking (ICSCAN)

作者： Kavya Sri. B K. Mahalakshmi Faculty of Information and Communication Engineering Anna university Chennai Department of Computer Science and Engineering KIT - Kalaignarkarunanidhi Institute of Technology Coimbatore India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

In modern cloud environments, Docker containers have become a cornerstone for efficient application deployment, enabling microservices architectures and seamless scalability. However, large-scale Docker environments present significant challenges, particularly in terms of security vulnerabilities and storage performance. This paper explores vulnerability analysis and storage performance optimization in high-performance, container-based Docker environments. Docker swarm is a container orchestration platform which could be very useful for maintaining the docker containers at scale even at distributed environment, these tools also provide us a great features for managing the rolling updates and health check in the lifecycle of containers. We perform a comprehensive evaluation of Docker image vulnerabilities using automated scanning tools, assessing common vulnerabilities and exposures (CVEs) across large image repositories. Additionally, we investigate storage performance bottlenecks and analyse the impact of various storage drivers on container efficiency. Our findings offer critical insights into balancing performance and security in high-scale Docker deployments, proposing strategies for mitigating risks and enhancing resource management. The results guide container orchestration decisions for enterprises aiming to deploy secure, high-performance systems.

关键词： Performance evaluation Runtime Scalability Containers Software Security Image storage Resource management Optimization Secure storage

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An In-Depth Study of Cybersecurity Threats and Intelligent Broadcasting Network Topology 1

An In-Depth Study of Cybersecurity Threats and Intelligent B...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Chauhan, Usha Gupta, Manali Galgotias University Department of Electrical Electronics and Communication Engineering Uttar Pradesh Greater Noida India Noida Institute of Engineering and Technology Department of Computer Science and Business System Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665456876

The smart grid has stacked ICT power equipment. Smart grids use modern electrical sensors. Future adoption of smart devices and their interaction with networks may offer cyberthreats. Several smart grid subsystems may be exposed if a component doesn't respond in real time, reducing efficiency and reliability. Superannuated networks create cyber security risks. This article explores a secure cyber infrastructure, related breakthroughs, and cyber security concerns in a complex system. Cybersecurity goals, objectives, and risk assessments are complete. These papers are summarized. Prioritize security. Then, examine smart grid hacks to uncover weaknesses and their repercussions. This research designed a cyber-security plan for breaches, counterattacks, and actions. Future research is discussed. © 2022 IEEE.

关键词： Cybersecurity

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An Evaluation and Implementation of Pretty Good Privacy (PGP) in Wireless Network Security 3

An Evaluation and Implementation of Pretty Good Privacy (PGP...

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Hans, Arvind Vigenesh, M. Yuvaraj, S. Bhagwat, Suvarna R. Zaki, Hamza Yamang, Chiging Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India Himalayan University Department of Bussiness & Management Itanagar India

ISBN: (纸本)9798350319125

Top privateers (PGP) is an encryption protocol that shields statistics saved, exchanged, or transmitted over wireless networks. PGP provides a green manner to comfy WiFi networks and communications using symmetric and publickey cryptography. This paper evaluates and examines the utility of PGP in wireless network protection. The paper critiques the layout and implementation of PGP on specific varieties of Wi-Fi networks, which include the partnerships to be had for implementing PGP in pre-present and destiny wireless infrastructures. Similarly to the numerous partnerships, the paper explores PGP's unique protocols and technologies and how they can be integrated inside Wi-Fi networks based on their extraordinary capabilities and functions. The paper additionally discusses the commercial advertising of PGP, its effect on wireless community protection, and the packages of PGP, in addition to a number of the demanding situations and threats related to its use. Moreover, the paper highlights the advantages of implementing PGP in wireless community safety. It gives a diffusion of trying out and implementation strategies that can be used to ensure a successful implementation. The paper concludes with a dialogue about the possible destiny directions of PGP and how it could be used to ease and protect wireless networks. © 2023 IEEE.

关键词： destiny infrastructures networks packages wireless networks

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Underwater estimation of audio signal prediction using fruit fly algorithm and hybrid wavelet neural network

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Journal of Reliable Intelligent Environments 2022年第2期8卷 211-221页

作者： Sagayam, K. Martin Ghosh, Ahona Bhushan, Bharat Andrew, J. Cengiz, Korhan Elngar, Ahmed A. Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computational Science Brainware University Kolkata India School of Engineering and Technology Sharda University Greater Noida India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Electrical and Electronics Engineering Traka University Edirne Turkey Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City62511 Egypt

Underwater acoustic signal is one of its most vital features in oceanography for predicting the local variants, helps to process the signal, and also assists to discover non-stationary signals. wavelet neural network (WNN) architecture acquires a huge benefit from the artificial neural network (ANN) and time-frequency localizing features of wavelet analysis (WA). However, the system could train using the ANN model, it has tendency of falling into local minima and generating convergence. A wavelet-aided neural network is integrated with a meta-heuristic optimizer named fruit fly algorithm (FFA) that aids our proposed system in which reduces the computational complexity and improves performance measures. The main objective of the proposed work is of modelling an optimized wavelet neural network for efficient measurement of the mean absolute error and the root mean square error. The simulation of the sound signals has been performed with the use of optimized neural networks that has predicted the unknown signal. The proposed model is assessed by comparing the outcomes with the complements of it, i.e. the traditional neural networks. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Forecasting

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On the Secrecy Performance of Reconfigurable Intelligent Surfaces-Assisted Satellite Networks Under Shadow-Rician Channels

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IEEE Transactions on Aerospace and Electronic systems 2025年第3期61卷 6794-6808页

作者： Le, Si-Phu Nguyen, Tan N. Le-Tien, Thuong Duy, Tran Trung Nguyen, Tien-Tung Ng, Derrick Wing Kwan Tu, Lam-Thanh Voznak, Miroslav Ton Duc Thang University Communication and Signal Processing Research Group Faculty of Electrical and Electronics Engineering Ho Chi Minh72900 Viet Nam VSB-Technical University of Ostrava Department of Telecommunications Faculty of Electrical Engineering and Computer Science Ostrava70800 Czech Republic University of Technology Vietnam National University Ho Chi Minh72900 Viet Nam Posts and Telecommunications Institute of Technology Faculty of Telecommunications 2 Ho Chi Minh72900 Viet Nam Industrial University of Ho Chi Minh City Faculty of Electronics Technology Ho Chi Minh70000 Viet Nam University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia

In this study, we investigate the secrecy performance of reconfigurable intelligent surfaces-assisted satellite (RISAS) networks. Specifically, we analytically derive closed-form expressions of the secrecy outage probability (SOP) and the tight lower bound secrecy rate (SR) exploiting the moment matching method (MMM). Furthermore, we explore the asymptotic behavior of the SOP as the satellite transmit power is sufficiently large, offering valuable insights into network performance. Numerical simulations are conducted to validate the established framework and elucidate the influence of critical parameters such as transmission distance, transmit power, and the number of reconfigurable intelligent surfaces (RIS) elements on SOP and SR. Our findings highlight the efficacy of increasing the number of phase shifters of the RIS to mitigate the adverse effects of fading, shadowing, and transmission environments. Finally, we unveil the adaptability of the proposed networks, illustrating their suitability for various satellite systems, including low earth orbit (LEO) and geostationary earth orbit (GEO). Among these, LEO offers superior performance due to its shorter transmission distance compared to medium earth orbit (MEO) and GEO. © 1965-2011 IEEE.

关键词： Satellite communication systems

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Optimization Models and Educational Teaching Research in Agricultural Logistics system 2

Optimization Models and Educational Teaching Research in Agr...

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2nd IEEE International Conference on Advances in Computing, communication and Applied Informatics, ACCAI 2023

作者： Karthick, S. Kumar, B. Anjanee Sethi, Puneet Singh, Niraj Kr Srivastava, Supriya Prasanth, P Vishnu Srm Institute of Science and Technology Department of Computer Science and Engineering Ghaziabad India Dadi Institute of Engineering and Technology Department of Electronic and Communication Engineering India Teerthanker Mahaveer University Moradabad India Amity University Amity Institute of Applied Sciences India Shoolini University Faculty of Management Sciences Solan India Mohan Babu University Tirupati India

ISBN: (纸本)9798350315905

A spic and span exhaustive disciplinesare logistics science. It is essential to the headway of the board and creation principles as well as the development of the public economy. To foster a powerful agricultural economy, the undertaking of developing new agricultural corporate substances is vital. The old techniques for agricultural item assembling and circulation are as of now not ready to fulfill the requirements of production network advancement and market informatization. To resolve the issues traditional strategies welcome, investigating new agricultural item creation and conveyance techniques is fundamental. By coordinating agricultural item store network assets, eliminating wasteful connections in the course of agricultural items, reducing expenses, accomplishing discernibility of agricultural items, and guaranteeing the quality and security of agricultural items, we make an advancement system for agricultural item supply chains, drove by enormous information specialist coops. Given this, this article proposes a model for a coordinated administration stage-based agricultural item store network, and it conducts broad research on the logistics activity rationale of the chain to give a hypothetical establishment and information support for the plan and improvement of the administration stage. © 2023 IEEE.

关键词： Supply chains

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A Machine Learning-Based Model for Energy Efficiency Classification of an Unmanned Aerial Vehicle 1

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Awotunde, Joseph Bamidele Arowolo, Micheal Olaolu Imoize, Agbotiname Lucky Farhaoui, Yousef Adeniyi, Abidemi Emmanuel Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara Ilorin240003 Nigeria Department of Computer Sciences Landmark University Omu-Aran Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria STI Laboratory T-IDMS Faculty of Sciences and Techniques Moulay Ismail University of Meknès Errachidia Morocco Department of Computer Sciences Precious Cornerstone University Ibadan Nigeria

ISBN: (数字)9783031262548

ISBN: (纸本)9783031262531

One of the most promising technologies for next-generation wireless networks is unmanned aerial vehicles (UAVs). They make excellent prospects for a range of applications due to their accessibility and capacity for establishing line-of-sight interactions with users. Because of the enormous amount of data available, artificial intelligence is rapidly developing and succeeding tremendously. As a result, a sizeable segment of the academic community has started integrating intelligence into the core of UAV networks by using machine learning (ML) approaches to address a range of drone-related problems. ML-based framework is designed to provide solutions to the different issues that have been time invariant when UAVs are utilized for communication. UAV-based delivery systems, real-time multimedia streaming, and intelligent transportation systems are all examples of this. The problems with wifi and security are revealed by this study. ML-based solution techniques are created to address these problems, enabling real-time secure operation of UAVs while leveraging wireless system resources in a flexible manner. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Traffic Congestion Prediction Based on Multivariate Modelling and Neural Networks Regressions 14

Traffic Congestion Prediction Based on Multivariate Modellin...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Fahs, Walid Chbib, Fadlallah Rammal, Abbas Khatoun, Rida Attar, Ali El Zaytoun, Issam Hachem, Joel Department of Computer and Communication Engineering IUL Wardanieh Lebanon LTCI France Faculty of Science Lebanese University Beirut Lebanon Kettering University 1700 University Ave FlintMI48504 United States

Smart traffic congestion reduction is actually a real challenge for big cities. Machine learning algorithms can play a significant role in traffic analysis, congestion prediction, and rerouting. In this paper, we propose a new prediction approach to reduce the traffic congestion problem by studying a scheme for predicting traffic flow information using four machine learning techniques: Feed Forward Neural Networks (FFNN), Radial Basis Function Neural Networks (RBFNN), simple linear regression model, and polynomial linear regression model. This prediction scheme is based on the following parameters: the average waiting time at entry and exit street pairs, the days of the week, hours of movement, holidays, and the rain rate. The results indicate that the FFNN technique overcomes the other techniques producing 97.6% prediction accuracy. © 2023 Elsevier B.V.. All rights reserved.

关键词： Traffic congestion

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An efficient intrusion detection system for SDN using convolutional neural network 2

An efficient intrusion detection system for SDN using convol...

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： Hassan, Heba A. Hemdan, Ezz E. El-Shafai, Walid Shokair, Mona El-Samie, Fathi E. Abd Menoufia University Faculty of Electronic Engineering Department of Electronics and Communication Engineering Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering

ISBN: (纸本)9781665418423

With the accelerated development of computer network utilization and the enormous growth of the number of applications running on top of networks, network security has become more significant. Intrusion Detection systems (IDS) are considered as essential tools that can be utilized to protect computer networks and information systems. Software-Defined Network (SDN) architecture is used to provide network monitoring and observation of functions. Generally, an IDS is developed to observe the regular traffic to the SDN in order to maintain a high level of security. This paper introduces an efficient IDS using Convolutional Neural Network (CNN). This IDS is applied on a new attack-specific SDN dataset called InSDN. The proposed IDS is compared in performance with different machine-learning-based systems such as Decision Tree Classifier (CART), Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF) classifier, and AdaBoost (AB) classifier. © 2021 IEEE.

关键词： Convolutional neural networks

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An Enhanced Residual Networks Based Framework for Early Alzheimer’s Disease Classification and Diagnosis 6th

An Enhanced Residual Networks Based Framework for Early Alzh...

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6th International Conference on communication, Devices and Networking, ICCDN 2022

作者： Awotunde, Joseph Bamidele Sur, Nath Samarendra Imoize, Agbotiname Lucky Misra, Sanjay Gaber, Tarek Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University East Sikkim Rangpo Majitar737136 India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany Department of Applied Data Science Institute of Energy Technology Halden Norway Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt School of Science Engineering and Environment University of Salford ManchesterM5 4WT United Kingdom

ISBN: (纸本)9789819919826

Alzheimer's disease (AD) is a long-lasting, degenerative brain illness for which there is now no successful treatment. However, there are medications that can delay its growth and stop its tracks. Therefore, stopping and regulating the progression of AD depends greatly on its early diagnosis. computer-aided diagnosis (CAD) in health care has become more and more dependent on deep learning technologies. This has help in early diagnosis and detection of various disease in health care. New techniques for the early diagnosis of neurocognitive abnormalities are now possible thanks to advancements in medical imaging and computational capacity in an effort to stop or slow down cognitive aging. Therefore, this study proposes a residual network (ResNet) type 50 for early diagnosis of AD. Convolutional neural networks (CNNs) are first used in the study for features extraction and then make the ultimate diagnosis using the ResNet-50. To create a better architecture, the problem of AD diagnosis will be addressed by utilizing and incorporating ResNet-50, which improves the current categorization problem's accuracy to its maximum. With a 98.8% accuracy rate, the proposed model using ResNet-50 produces encouraging findings for AD prediction. The study reveals that the model can effectively circumvent CNNs for the diagnostic issue with AD. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer aided diagnosis

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