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检索条件"任意字段=9th International Conference on Distributed Computing and Networking"

共 2734 条记录，以下是31-40 订阅

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A Novel Color Image Encryption based on Chaos and DNA Mutation

A Novel Color Image Encryption based on Chaos and DNA Mutati...

引用

9th international conference on Signal and Image Processing (ICSIP)

作者： Sun, Mingliang Yuan, Jie Li, Xiaoyong Liu, Dongxiao Wei, Xinghai Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv BUPT Beijing Peoples R China

ISBN: (纸本)9798350350920

the security of the color images is challenged by the illegal copying and distribution over various communication networks. this work proposed a novel color image encryption scheme based on the chaos and Deoxyribonucleic acid (DNA). Firstly, A novel four dimensions chaotic system is constructed and analyzed. Secondly, the DNA coding rules are controlled by the chaos-based pseudorandom sequence to substitute the image pixels. the column and row rotation are designed to disturb the image pixels. Finally, the DNA matrix coded from the color image is divided into blocks to implement the XOR and addition operation with the pseudorandom sequence. Its security is simulated and compared with other schemes. the comparison show that the proposed scheme has a higher key sensitivity, lower correlation of adjacent pixels, and higher NPCR and information entropy. therefore, the proposed image encryption scheme is more capable against statical attacks and efficiency while keeping high robustness.

关键词： chaos DNA coding image encryption pseudorandom sequence

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By-default Security Orchestration on distributed Edge/Cloud computing Framework 9

By-default Security Orchestration on distributed Edge/Cloud ...

引用

9th IEEE international conference on Network Softwarization (IEEE NetSoft) - Boosting Future Networks through Advanced Softwarization

作者： Bernabe Murcia, Jose Manuel Perez Zarca, Jose Francisco Molina Zarca, Alejandro Skarmeta, Antonio Univ Murcia Dept Informat & Commun Engn Murcia Spain

ISBN: (纸本)9798350399806

Next generation networks and the strength of the distributed computing paradigm (edge/cloud) are transforming how services are provisioned, mainly when solutions focus on collaboration and aggregation of resources provided by different entities or organisations, that becomes essential to satisfy the most demanding computation and storage service requirements. However, it also entails challenges such as infrastructure and technologies heterogeneity, which directly impacts infrastructure management and especially security, that usually tends to be relegated to a second place. this paper provides a by-default security orchestrator approach to mitigate the above mentioned challenges in distributed edge/cloud computing frameworks. We use an Intent-based/policy-based orchestration paradigm for dealing with heterogeneity, allowing users to request service deployments securely without requiring knowledge about the underlying distributed infrastructure. By-default security orchestration will decide how to provide the requested services, ensuring that they are compliant with the security requirements provided by the user and the ones gathered by the system, locally and from reliable external sources (1). We provide design and use-cases based workflows for managing by-default security orchestration in proactive and reactive ways. In the future, it is expected to perform the implementation and validation of the proposed approach inside the scope of the FLUIDOS EU project.

关键词： Edge Cloud computing Orchestration NFV SDN

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Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma 9

Effective Machine Learning Methods for the Detection of Pulm...

引用

9th international conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Poornima, G. Anand, L. School of Computing Srm Institute of Science And Technology Dept of Networking And Communications Tamilnadu Kattankulathur India

ISBN: (纸本)9798350365092

the escalating mortality rate attributed to lung cancer is exacerbated by its inflammatory nature, fostering uncontrolled cellular proliferation within lung tissues. While complete eradication remains a challenge, proactive screening holds promise for mitigating its impact. this study is dedicated to exploring a repertoire of machine learning classification methodologies, with a primary focus on identifying effective approaches. Leveraging advanced imaging techniques, particularly CT images, this study aims to ascertain the most reliable means of diagnosing lung cancer. through rigorous comparison and evaluation, we endeavor to detail comparisons of machine learning algorithms on effective detection frameworks that can significantly enhance screening protocols. © 2024 IEEE.

关键词： Computerized tomography

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Advancing threat Detection in Fog computing: A Comprehensive Approach to Real-Time Analysis and Model Generation 9

Advancing Threat Detection in Fog Computing: A Comprehensive...

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9th IEEE international conference on Fog and Mobile Edge computing (FMEC)

作者： Cuadrillero Paredes, Lucia Perales Gomez, Angel Luis Canovas, Oscar Fernandez Malmo, Lorenzo Garcia Clemente, Felix J. Univ Murcia Fac Comp Sci Murcia Spain

ISBN: (纸本)9798350366495;9798350366488

Fog computing, an extension of cloud computing, places computational resources closer to data sources, enhancing network efficiency and security. this paper addresses the challenges posed by cybersecurity threats within fog computing environments, specifically in managing vast volumes of network traffic from IoT devices which are vulnerable to cyberattacks. We propose a novel architecture for real-time anomaly detection that integrates fog and cloud computing layers. this two-tiered system utilizes machine learning and deep learning models for dynamic and adaptive threat detection. By leveraging the computational power of modern GPUs, the system analyzes network traffic at speeds up to 10 Gbps, enabling the detection of anomalies before they can cause significant damage. We introduce a distributed anomaly supervisor that operates at the fog level to provide real-time analysis and immediate response to potential threats, while cloud-level operations facilitate a comprehensive overview and coordination. the effectiveness of this architecture is demonstrated through extensive experimentation, proving its capability to manage high-speed data transfers and improve security measures across fog networks.

关键词： anomaly detection deep learning machine learning malware real-time detection

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Federated Learning in Heterogeneous Cloud computing Environment 9

Federated Learning in Heterogeneous Cloud Computing Environm...

引用

9th international conference on Big Data and Information Analytics, BigDIA 2023

作者： Li, Yaoyu Jin, Shuai Zhu, Jianghan National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350330076

this study examines key characteristics of cloud computing technology within the domain of federated learning, with the primary objective of exploring the principles concerning distributed computing acceleration ratio and storage input/output efficiency across various network segments. Combining the heterogeneous characteristics of cloud segments, we present a novel approach for distributed model training strategies by using the federated learning technique. this approach can keep the accuracy of the model, enhance training efficiency, and protect user privacy. the experiment demonstrates that the proposed strategy outperforms the baseline method, which does not account for variations in different segments. © 2023 IEEE.

关键词： Cloud computing

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Designing Fault-tolerant Modern Data Engineering Solutions with Reliability theory as the Driving Force 24

Designing Fault-tolerant Modern Data Engineering Solutions w...

引用

9th international conference on Machine Learning Technologies (ICMLT)

作者： Virmani, Ankit Kuppam, Manoj Inst Elect & Elect Engineers Piscataway NJ 08854 USA

ISBN: (纸本)9798400716379

this study has been undertaken to amalgamate the principles of Site Reliability Engineering and Data Engineering to effectively measure, monitor and manage the reliability of petabyte-scale data engineering process from collection at source to processing, analyzing, and distributing the data for appropriate decision making to improve business outcomes and system performance. Modern data architectures increasingly leverage cloud platforms, low-code systems, and serverless technologies to enable scalable data engineering. However, these innovations also introduce new complexities regarding reliability assurance. As these failure-prone yet business-critical data infrastructures continue rapid adoption, it is vital to elucidate architectural paradigms, quantified benchmarks, and procedural methodologies tailored to safeguarding dependability across heterogeneous, distributed data ecosystems. this paper will equip end users with a reusable framework ingrained with best practices to develop a blueprint for data reliability across business units of an organization.

关键词： Big Data Engineering Site Reliability Engineering Data Operations Platform Management Cloud computing

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Design and Implementation of a Slice as a Service Architecture on the Edge Cloud with Resource Constraints 9

Design and Implementation of a Slice as a Service Architectu...

引用

9th IEEE international conference on Network Softwarization (IEEE NetSoft) - Boosting Future Networks through Advanced Softwarization

作者： Azevedo, Rodrigo Ferraz de Paula, Luciano Bernardes Verdi, Fabio Luciano Univ Fed Sao Carlos Dept Comp Sci Sorocaba Brazil Fed Inst Sao Paulo Braganca Paulista SP Brazil

ISBN: (纸本)9798350399806

Cloud Network Slicing (CNS) is a concept that describes a mechanism to provide computing, networking, and storage as a virtual slice entity, enabling new approaches to applications and structuring resources at the edge of the network. In this paper, the architecture defined in the NECOS Project is adopted, and the functions for creating CNS in resource-constrained edge devices were designed and implemented. the implementation was evaluated on Single Board Computers (SBCs), using lightweight virtualization solutions (microservices) and the results achieved show that it is possible to instantiate CNSs on that hardware, however, also show some limitations of multiple slice support on resource-constrained devices.

关键词： cloud network slicing microservices single board computer cloud computing edge computing IoT

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distributed Key Value Store for IoT Edge Devices 9

Distributed Key Value Store for IoT Edge Devices

引用

9th international conference on Computer Science and Engineering, UBMK 2024

作者： Aslantaş, Burak Pektaş, Elif Nurdan Baydere, Şebnem Yeditepe University Dept. of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350365887

the proliferation of the Internet of things (IoT) requires efficient and scalable data management solutions at the edge of the network. Edge devices play a critical role in processing data and making real-time decisions, thereby minimizing latency and reducing the load on central servers. However, these devices face challenges such as scalability, latency, and fault tolerance. this paper proposes a distributed key-value store specifically designed for IoT edge devices to address these challenges. By integrating distributed key-value stores with IoT edge devices, we aim to enhance data access and management efficiency. Our approach includes the development of optimal methodologies and architectural standards for this integration, addressing key issues such as data consistency, fault tolerance, and low latency. through prototype creation and testing, we demonstrate significant improvements in data processing speed and system reliability. the ultimate objective of this research is to advance edge computing for IoT, fostering more robust, scalable, and efficient systems. © 2024 IEEE.

关键词： Data consistency

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Simulation for urban computing scenarios: An overview and research challenges 10

Simulation for urban computing scenarios: An overview and re...

引用

10th IEEE international conference on Cyber Security and Cloud computing and 9th IEEE international conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Wu, Lin Yang, Guogui Yan, Jintao Ran, Shuguang Zhao, Baokang Zhou, Huan National University of Defense Technology Changsha410073 China

ISBN: (纸本)9798350312461

Urban computing is an emerging computing paradigm for processing urban data to serve urban applications, which is an important manner to realize smart cities. To be specific, cloud computing and edge computing are usually leveraged to support urban computing. As a distributed and extended solution for cloud computing, edge computing is capable of providing better data services for IoT devices. However, edge computing resources are widely distributed and edge devices are highly mobile. To guarantee the QoS (Quality of Service), it is challenging to decide where to distribute the computing resources according to edge devices, especially around a specific urban area. therefore, it is essential to simulate and verify the solution through evaluation tools before practical deployment in urban scenarios. But urban computing is a relatively new paradigm, there are few evaluation tools for direct usage. To provide hints for useful tools, we perform a survey study on existing evaluation tools for edge computing, which can be somehow applied to urban computing. In this paper, we first classify and define the evaluation tools into three categories: simulators, emulators, and testbeds. then we perform a comprehensive study on the above three categories. Finally, we conclude the research challenges for urban computing based on current evaluation tools. © 2023 IEEE.

关键词： Testbeds

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Research on Unknown distributed Denial-of-Service Attacks Intrusion Detection Based on Deep Learning 9

Research on Unknown Distributed Denial-of-Service Attacks In...

引用

9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Zhang, Liumei An, Zhaoyang Hu, Yuanjiao Xi'an Shiyou University School of Computer Science Xi'an China

ISBN: (纸本)9798350376548

the increasing prevalence of distributed Denial-of- Service (DDoS) attacks poses a serious threat to network security, emphasizing the crucial need for effective intrusion detection methods. the continuous evolution of attack strategies has exposed the limitations of traditional feature engineering and statistical analysis in detection mechanisms. To address this challenge, this study proposes an innovative DDoS attack detection framework based on deep learning. At the core of this framework lies a Temporal Convolutional Network (TCN) structure, cleverly integrated with an attention mechanism to enhance the model's ability to identify and extract crucial information. this integration not only aids in capturing long- term dependencies and global patterns in time-series data but also significantly improves detection accuracy and system robustness. To validate the effectiveness of the proposed method, comprehensive experimental evaluations were conducted using the CICDDoS2019 dataset. the results demonstrate a notable achievement, with our model achieving a high classification accuracy of 98.14%. In comparison to traditional machine learning methods, the model exhibits significantly reduced false positive and false negative rates. © 2024 IEEE.

关键词： Network intrusion

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