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检索条件"任意字段=3rd International Conference on Distributed Computing and Internet Technology"

共 8195 条记录，以下是81-90 订阅

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Deploy a Secure Chat Application for Log Monitoring in Kubernetes 3

Deploy a Secure Chat Application for Log Monitoring in Kuber...

引用

3rd international conference on Intelligent Data Communication Technologies and internet of Things, IDCIoT 2025

作者： Dinesh, S. Nivedha, R. Mahalakshmi, S. Balasaigayathri, B. Department Of CSE Cyber Security Sathyabama Institute of Science and Technology Chennai India School of computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

This paper presents the development and deployment of a secure chat application in a Kubernetes environment for real-time log monitoring and communication. The research addresses the challenges of managing and aggregating logs in a distributed, containerized environment, specifically within Kubernetes clusters. Traditional logging solutions often struggle with providing real-time collaborative troubleshooting, centralized log aggregation, and robust security. The proposed system integrates Kubernetes-native features, along with Fluentd and the ELK stack (Elasticsearch, Logstash, Kibana), to centralize log management and provide a unified interface for real-time communication through WebSockets. The application is designed to ensure high scalability, fault tolerance, and security by implementing TLS encryption and Role-Based Access Control (RBAC). The outcome is a scalable, secure, and efficient log monitoring solution that improves operational efficiency, enhances system reliability, and fosters collaborative debugging, making it a valuable tool for developers and administrators in Kubernetes-based environments. © 2025 IEEE.

关键词： Fault tolerance

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Secure Channel Creation for Task Delivery in Vehicular Cloud computing to Avoid Denial of Service (DoS) 3

Secure Channel Creation for Task Delivery in Vehicular Cloud...

引用

3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Meenambika, A. Pavalarajan, S. Lavanya, N. Anu Jerom, B. Ajith Divya, S. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Data Science PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331532420

Vehicular Cloud computing (VCC) offers a promising platform for supporting various automotive applications and services. However, its distributed and dynamic nature makes it susceptible to Denial-of-Service (DoS) attacks, particularly during channel establishment. This research proposes a comprehensive approach to mitigate these risks by integrating a suite of security mechanisms. These include rate-limiting, resource management, traffic analysis, access control, authentication, encryption, intrusion detection, behavioral analysis, and blacklisting/whitelisting techniques. By incorporating these measures, the proposed approach enhances the security and reliability of VCC, ensuring the availability of critical automotive services and protecting vehicles from the impact of DoS attacks. © 2024 IEEE.

关键词： Cloud platforms

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Feature Extraction to Evaluate the Quality of Data Using Machine Learning Technique 3

Feature Extraction to Evaluate the Quality of Data Using Mac...

引用

3rd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pratheesh, R. Divya, V. School Of Computing Sciences Vels Institute Of Science Technology & Advanced Studies Chennai India School Of Computing Sciences Vels Institute Of Science Technology & Advanced Studies Department Of Information Technology Chennai India

ISBN: (纸本)9798350318609

Data quality analysis is a crucial step in ensuring the reliability and accuracy of data used in machine learning models. The proposed approach utilized with machine learning algorithms to identify outliers in the data. Outliers can indicate errors are anomalies that might affect the quality of the dataset. Data Profiling is used to identify patterns and distributions. Missing Data Imputation predict the missing values. Data Validation and Cleansing, Data Duplication Detection, Inconsistency Detection are considered as the primary analysis to be done. The feature extraction process read the sample test dataset, pre-process the dataset by cleaning the data for data duplication, inconsistency through dynamic algorithms principal component analysis (PCA), Linear Discriminant Analysis (LDA) and independent component analysis (ICA) is considered here. The proposed feature extraction data is further opted for pattern validation through isolated forest algorithm. The performance of the system is evaluated using accuracy, precision, and Recall. The LDA model outperforms with accuracy of 99% for the given testset. © 2024 IEEE.

关键词： Feature extraction

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DDoS Attacks Detection in IoT Networks using Naive Bayes and Random Forest 3

DDoS Attacks Detection in IoT Networks using Naive Bayes and...

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3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Srivastava, Animesh Tiwari, Shweta Rawat, Bhupender Singh Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Institute of Engineering Chandigarh University Department of Computer Science & Engineering Department / University Punjab Gharuan Mohali India Coer University College of Smart Computing Roorkee India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350375190

The proliferation of internet of Things (IoT) devices has resulted in numerous benefits, including streamlined tasks and improved connectivity but has also raised concerns regarding security vulnerabilities. distributed Denial of Service (DDoS) attacks targeting IoT systems pose a significant threat, potentially disrupting operational activities. This study conducts a comparative analysis of existing methodologies to detect DDoS attacks in IoT environments, focusing on the efficacy of machine learning algorithms. Specifically, this study evaluates the performance of Random Forest and Naive Bayes classifiers in detecting DDoS attacks using an IoT dataset. The evaluation involves training and testing these classifiers and assessing their accuracy as a key performance metric. Empirical findings demonstrate enhanced precision in identifying DDoS attacks in IoT environment, indicating the effectiveness of the proposed approach. By leveraging machine learning algorithms, particularly Random Forest and Naive Bayes, traditional techniques for detecting and mitigating DDoS attacks in IoT environments are accelerated, thus enhancing the security of IoT systems. © 2024 IEEE.

关键词： internet of things

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Architecture Design of Electronic Commerce System Based on distributed technology 3

Architecture Design of Electronic Commerce System Based on D...

引用

3rd international conference on Computer, Control and Robotics, ICCCR 2023

作者： Suo, Haiyan Chen, Tao Jiangsu Provincial People's Hospital Jiangsu Nanjing China Co. LTD Jiangsu Nanjing China

ISBN: (纸本)9781665492126

Because of the small number of users, low concurrency and small amount of data in the early electronic commerce system, the application software only needs to deploy a single server to meet the throughput requirements. With the continuous growth of user scale and business volume, enterprises gradually adopt distributed architecture to improve the availability of electronic commerce system, and use multiple cheap machines to complete complex computing or storage tasks. In recent years, with the continuous maturity and development of cloud native technologies such as microservice technology and container cluster management technology, major internet companies have followed the 'big middle platform strategy' and built 'cloud platforms' that adapt to their own organizational structures to cope with market changes and make flexible and rapid strategic adjustments. In general, the main driving factor of electronic commerce system evolution is the huge number of users faced by products. Cloud native is an extension of distributed architecture. This paper introduces the development stage of software distributed architecture, designs and implements distributed electronic commerce system. © 2023 IEEE.

关键词： Electronic commerce

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Anticipating Customer Churn in Telecommunication Using Machine Learning Algorithms for Customer Retention 3

Anticipating Customer Churn in Telecommunication Using Machi...

引用

3rd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Rajarajan, K. Vishal Reddy, D. Priya, S. College Of Engineering And Technology Srm Institute Of Science And Technology Department Of Computing Technologies Kattankulathur India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

Customer attrition is a significant issue and a top priority for large corporations. This study addresses this pressing issue. The dataset is obtained from Kaggle which constitutes of training set and testing set, 80 percent and 20 percent of the entire dataset respectively for identifying customers who tend to unsubscribe in the telecommunications industry. Predicting customer churn with precision is a difficult endeavor, mostly due to the dependence on a singular prediction model in most existing projects. To address this, this study proposes a novel approach that compares multiple prediction models like logistic regression, KNN, random forest, SVV, Gaussian NB, Kernel SVM, Support Vector Machine, to estimate customer churn. With ROC AUC Mean score of 84% for Logistic Regression produced a better result. In the future, the study might be expanded to investigate the evolving behavioral patterns of churn consumers with the application of Artificial Intelligence approaches for predictive and trend analysis to preserve valuable client. © 2024 IEEE.

关键词： Logistic regression

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Delay-Optimized Task Scheduling Based on Deep Reinforcement Learning in computing Power Networks 3rd

Delay-Optimized Task Scheduling Based on Deep Reinforcement ...

引用

3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Tang, Qinqin Xu, Deyun Xie, Renchao Feng, Li Chen, Tianjiao Huang, Tao Beijing University of Posts and Telecommunications Beijing China China Mobile Research Institute Beijing China

ISBN: (纸本)9789819624676

With the rapid advancement of information and communication technology, there arises an increasing demand for heightened computing capabilities to facilitate a myriad of intelligent services. The computing power network (CPN) amalgamates an array of resources including cloud and edge devices to enable collaborative sensing, routing, and scheduling. This integration significantly amplifies the utilization of computing network resources while concurrently augmenting service quality. In the realm of multi-node distributed joint computing within CPNs conventional scheduling methodologies frequently neglect the interplay between computing and the network, resulting in suboptimal task scheduling. To address this issue, this study proposes a delay-optimized task scheduling mechanism based on deep reinforcement learning. Simulation results demonstrate that this framework achieves faster convergence speed, particularly under dual-depth networks, outperforming other algorithms in reducing system delays, energy and enhancing scheduling efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computing power

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Construction and Operation of a Generative Artificial Intelligence-Assisted Language Learning System 3

Construction and Operation of a Generative Artificial Intell...

引用

3rd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Cao, Yilu Lu, Haoyu Zhou, Yingping Gao, Jiahui Wang, Han Jiao, Yabei College Of Foreign Languages Chongqing University Of Technology Chongqing China College Of Computer Science And Engineering Chongqing University Of Technology Chongqing China School Of Accountancy Chongqing University Of Technology Chongqing China Liangjiang International College Chongqing University Of Technology Chongqing China

ISBN: (纸本)9798350318609

The generative artificial intelligence-assisted language learning system is a system that utilizes artificial intelligence technology. It provides users with more personalized and efficient language learning services through core modules such as generative pre-trained models and personalized learning engines, It is currently one of the significant applications of artificial intelligence technology in the field of language learning. This paper aims to introduce the design, construction, and operation of a generative artificial intelligence-assisted language learning system. In this system, the generative pre-trained model GPT -3.5 is employed, incorporating core modules such as natural language processing, generative adversarial networks, and Transformer models. This paper also conducts detailed research and analysis on aspects such as text generation quality in the system, response time testing of the model within the system, and feedback on system application scenarios. Through the research presented in this paper, valuable reference and guidance can be provided for the development and application of generative artificial intelligence-assisted language learning systems. © 2024 IEEE.

关键词： Engines

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A Novel Fair Frame Time Slot LoRaWAN internet of Things 3

A Novel Fair Frame Time Slot LoRaWAN Internet of Things

引用

3rd international conference on Intelligent computing and Next Generation Networks, ICNGN 2024

作者： Almuhaya, Mukarram Al-Hadhrami, Tawfik Kaiwartya, Omparakash Brown, David J. Department Computer Science Nottingham Trent University Nottingham United Kingdom

ISBN: (纸本)9798331529222

The technologies underpinning low-power wide-area networks (LPWANs) play a crucial role in IoT applications because they fulfil the four major facets that afford successful IoT deployments: long-range, free frequency ISM bands, low-cost, and low energy consumption. Industrial and academic sectors alike have shown keen interest in LoRaWAN technology due to its networking independence and open standard specification among LPWAN options. In spite of this, LoRaWAN has a scaling difficulty when it comes to supporting a large number of devices. This challenge arises from the increased collision risk that is inherent in its Aloha-based physical layer. The performance deteriorates further when some devices use their energy sooner than others, owing to the disparate allocation of spreading factors, hence reducing the network’s lifetime. This article presents a novel fair frame scheduling method for allocating service functions to nodes in six parallel frames distributed timely on six frequency band channels with the objective of minimising total data collection time while adhering to radio duty cycle constraints. The result shows that fair frame FF-LoRa minimizes the time of maximum frame (Time a round) to approximately 25%, which makes the farthest device more power efficient and leads to enhancement of the network lifespan. ©2024 Crown.

关键词： Energy utilization

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Blockchain Security Using Q-Pragmatic-Sensitive Feature Analysis to Preserve Privacy 3

Blockchain Security Using Q-Pragmatic-Sensitive Feature Anal...

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3rd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Punithavathi, S. Jeyalaksshmi, S. Vels Institute Of Science Technology & Advanced Studies Department Of Computer Science Chennai India School Of Computing Science Vels Institute Of Science Technology & Advanced Studies Department Of Information Technology Chennai India

ISBN: (纸本)9798350318609

Healthcare data contains large amounts of personal information and is privacy sensitive. The healthcare sector needs privacy protection to keep private and sensitive information safe from unauthorized access. Security problems are raised by fundamental sharing difficulties involving personal information setting to securely forecast sensitive medical data using blockchain technology to address this issue. To solve the problem issue, propose a quasi-sensitive identification of blockchain security using tried-and-true partition fold padding encryption (PFPE) to improve the security of healthcare transactions. The Q-Pragmatic-Sensitive Feature Analysis (QPSFA) determines the terms related to sensitive privacy features. Finally, the Provable Fold Padding Encryption, and the authentication procedure is completed by using an Access Verification-based role to verify it. To ensure that, in a blockchain, the private data are cleansed and placed into data blocks. This suggests that the proposed system outperforms high accuracy up to 90%, as well security up to 94 % high insecurity compared to previous approaches regarding sensitivity and specificity rate performance and prediction accuracy. © 2024 IEEE.

关键词： Blockchain

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