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检索条件"任意字段=8th International Conference on Distributed Computing and Networking"

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distributed and Biometric Signature-Based Identity Proofing System for the Maritime Sector 8

Distributed and Biometric Signature-Based Identity Proofing ...

引用

8th international conference on Computer Science and Engineering, UBMK 2023

作者： Akbas, Taylan Koltuksuz, Ahmet Yucel, Cagatay School of Eng. Yasar University Dept. of Computer Engineering Izmir Turkey Bournemouth University Dept. of Computing and Informatics BH12 5BB United Kingdom

ISBN: (纸本)9798350340815

the maritime sector is an industry that faces significant and various challenges related to cyber security and data management, such as fraud and user authentication. therefore, there is a need for a secure solution that can effectively manage data transactions while resolving digital identity. A biometric signature application in blockchain for fighting fraud and fake identities may provide a solution in the maritime sector. this research proposes a biometric signature and an IPFS network-blockchain framework to address these challenges. this paper also discusses the proposed framework's cyber security challenges that threaten behavioral biometric security. © 2023 IEEE.

关键词： Smart contract

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Lightweight Fog Attention Graph Convolutional Network for Secure Data Aggregation Mechanism to Fog based Cloud Assisted IoT Network 8

Lightweight Fog Attention Graph Convolutional Network for Se...

引用

8th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Akilesh Kumar, S. Hariharan, P. Sukhel Ahamed, N. Gokilavani, S. Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

Ever Increasing demand of Cloud computing paradigm has resulted in widespread development and deployment of multiple fog nodes to cloud assisted internet of things networks as it is highly capable of providing the users with on demand and low latency services through mobile collaborative devices in the edge of multiple clouds. Mobile Collaborative Devices is used for fog computing with integrated storage, computing and communication capabilities. However it offers improved efficiency and increased flexibility. Despite of multiple advantageous of fog based cloud assisted internet of things, security of the user data and their privacy can be compromised during data aggregation. In addition, it faces huge challenges in effectively aggregating the data and transmitting to the cloud server on establishing the strong security. In order to enhance the security of the user data and to preserve the privacy of the user information along establishing a secure data aggregation and transmission, a new light weight convolutional attention network is proposed in this article. It establishes secure data aggregation and data transmission to the distributed cloud data centers efficiently. Fog attention graph convolutional network leverages neighbour fog data efficiently and securely from dataset and represents in form of graph. Attention coefficient is assigned to each fog data represented in graph structure and attention score is calculated to each update of the nodes in graph. Specifically softmax function employs ID3 decision tree classifier and K anonymization mechanism to aggregates fog data on basis of updates of the attributes of the fog nodes in terms of the attention score and transmits aggregated data to the distributed cloud server. Further proposed model enhance the security of the data aggregation and transmission with index function through hash mechanism. Experimental analysis is carried out on the Fog assisted IoT medical (FIoMT) dataset. Dataset composed of patient h

关键词： Fog computing

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SSDAEE: Semi-Supervised Data-Augmented Event Extraction for Transfer Learning 8

SSDAEE: Semi-Supervised Data-Augmented Event Extraction for ...

引用

8th international conference on Data Science in Cyberspace, DSC 2023

作者： Shen, Yueyue Song, Feifan Wu, Jingchen Wu, Xu School of Cyberspace Security Beijing University of Posts and Telecommunications Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Economics and Finance Changchun Finance College Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Jilin China School of Computer Science Beijing University of Posts and Telecommunications Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9798350331035

Although the general event extraction model has achieved good results in the standard data sets, there is a gap in the extraction results in low-resource environments. the general event extraction model still has room for improvement in the performance of special fields. therefore, this paper proposes a semi-supervised reading comprehension event extraction model adapted to transfer learning, based on improved UDA for data enhancement, called SSDAEE, which can increase the accuracy of reading comprehension event type recognition. SSDAEE has a good performance on the public opinion data set. It can not only enhance a small number of supervised data in low-resource fields with unsupervised data but also improve the generalization ability of the model. Experiments show that when only 5% unsupervised data is used, the F1 score of SSDAEE on the public opinion data set is increased by 9% compared with the fully supervised model, and by 2% compared with other semi-supervised models. © 2023 IEEE.

关键词： data augmentation event extraction semi-supervised transfer learning

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A Novel Cluster Control Method for Effectively Improving the Hosting Photovoltaic Capacity of Distribution Networks 8

A Novel Cluster Control Method for Effectively Improving the...

引用

8th IEEE international conference on Energy Internet, ICEI 2024

作者： Li, Haobin Li, Yiquan Zhao, Ruifeng Guo, Ruifeng Chen, Zhiwei Pan, Kaiyan Wu, Haiyong Guangdong Power Grid Co. Ltd Electric Power Dispatching and Control Center Guangzhou China Dongfang Electronics Cooperation Yantai China

ISBN: (纸本)9798331523558

To handle the problem of not considering the photovoltaic hosting capacity for cluster control in distribution networks, this paper proposes an effective cluster control strategy to improve the distributed photovoltaic (PV) hosting capacity. Firstly, based on a "cloud-cluster-end"architecture, a cloud optimization model aiming at minimizing PV curtailment and the voltage deviation of dominant nodes is established to improve PV accommodation. Secondly, a cluster optimization model aiming at minimizing power loss is established to implement coordinated optimization between cloud and clusters. Different from previous studies, the PV outputs from the cloud optimization are used as the constraints for the cluster optimization. Finally, the proposed model is converted from the nonlinear programming into a linear programing problem using the second-order cone relaxation technique combined with the polygon interior approximation method (PIAM). the accuracy and efficiency of the proposed method compared to existing methods are verified through validation on the actual IEEE151-bus system. © 2024 IEEE.

关键词： Cloud computing architecture

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Cloud computing and its Application in the Future Fire Control System of Ground Combat Platform 8

Cloud Computing and its Application in The Future Fire Contr...

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8th international conference on Big Data and Information Analytics, BigDIA 2022

作者： Wang, Quandong Xiao, Bo Niu, Zhiyi Center for Strategic Evaluation Consultation Academy of Military Sciences Beijing China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

this paper first discusses the concept, architecture and technical advantages of cloud computing. Secondly, focusing on the operational requirements of future information warfare, the development trend of existing fire control platforms is analyzed and the concept of cloud fire control is proposed. Taking the ground combat platform as the object, the basic principle, system structure and composition of the cloud fire control of the ground combat platform are analyzed and designed in depth. Finally, the key technologies of distributed dynamic networking, mobile cooperative awareness, and multi-platform collaborative attack and decision-making that need to be solved in cloud fire control system are pointed out, which can provide reference for the development of cloud fire control system for future ground combat platforms. © 2022 IEEE.

关键词： Fire control systems

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DDoS Attacks Detection in Multi-Controller Based Software Defined Network 8

DDoS Attacks Detection in Multi-Controller Based Software De...

引用

8th international conference on Web Research, ICWR 2022

作者： Valizadeh, Parisa Taghinezhad-Niar, Ahmad University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9781665466264

With the rapid growth of computer devices, network communication faced different challenges from network management to traffic engineering. Software-Defined networking (SDN) is a well-known solution for optimizing these communications. SDN is a new networking architecture to simplify network management that separates the control plane from the data plane. the central controller is the major advantage of SDN;however, it has security vulnerabilities such as being unreachable in distributed Denial-of-Service attacks (DDoS). Consequently, it is very important to protect SDN from DDoS attacks. In this paper, we proposed an algorithm for DDoS attack detection and reducing its impact in SDN architecture with multiple distributed controllers. We presented two methods 1) the entropy of destination IP addresses and 2) Packet window initiation rate for early detection of DDoS. We used Mininet and floodlight to simulate our algorithm in different scenarios. the result shows that our algorithm outperforms other works in various network configurations and multi-victim attacks. © 2022 IEEE.

关键词： Software defined networking

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Bias-Resilient Elephant Flow Detection in distributed SDNs through Federated Learning 8th

Bias-Resilient Elephant Flow Detection in Distributed SDNs ...

引用

8th international conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Boussaoud, Kaoutar Bellouch, Mohamed Ayache, Meryeme En-Nouaary, Abdeslam Rabat Morocco

ISBN: (纸本)9789819702091

this paper presents an innovative approach based on Federated Learning (FL) for detecting Elephant/Mouse Flows within distributed Software-Defined networking (SDN) environments. In such networks, multiple domains are overseen by individual SDN controllers. Traditional centralized Machine Learning (ML) methods for detecting these flows involve training separate local models on data specific to each domain. this can lead to biased local models due to their dependence on context during data preparation. Consequently, these local models may struggle to adapt to different contexts and may not effectively handle the complexities of modern networks. To address these challenges, the proposed approach leverages the power of FL by fostering collaboration among controllers from various domains. this collaborative process aims to alleviate issues of local bias and, in turn, introduce greater adaptability and generalization to the detection process. through experiments, we demonstrated the impact of data heterogeneity on the performance of local models and how the FL approach effectively mitigates these issues. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software defined networking

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Research on Model Optimization Technology of Federated Learning 8

Research on Model Optimization Technology of Federated Learn...

引用

IEEE 8th international conference on Big Data Analytics (ICBDA)

作者： Dai, Hang Hong, Yusheng Univ Sci & Technol China Sch Software Engn Suzhou Peoples R China

ISBN: (纸本)9798350310764

Federated learning is a new distributed machine learning model training method, which can protect user privacy while enabling a large number of edge devices to train a shared model based on their local data sets, and enable participants to benefit from each other. However, federated learning also faces some serious challenges, such as resource constraints, uneven data distribution, edge dynamics, and so on. Moreover, the existing federated learning methods may lead to long training time, consume a lot of bandwidth and other resources, and the heterogeneity of edge devices further worsens the inherent synchronization barrier problem of federated learning. In response to the above problems, this paper studies the existing model optimization technologies of federated learning, including node selection mechanism, local data enhancement, model compression, local update, gradient compression and other model optimization technologies, and summarizes and analyzes them.

关键词： Federated learning Edge computing Model optimization

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Intelligent System for distributed Deep Learning Training Traffic Optimization 24

Intelligent System for Distributed Deep Learning Training Tr...

引用

24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Lu, Weipeng Wu, Shuang Ou, Junhui Niu, Bing He, Jin Song, Kevin Zheng, Xinwei Ustc Hefei China Vmware Beijing China

ISBN: (纸本)9798350319934

distributed training crossing multiple computing nodes and accelerators has been the mainstream solution for large model training. Precedent work on distributed deep learning (DDL) training acceleration has focused on improving the efficiency of communication and GPU resource scheduling, with little attention given to network bandwidth usage. However, due to cluster GPU fragmentation, more than one training job is frequently assigned to the same node to reduce waiting time. the competition for network bandwidth among various distributed training jobs becomes a problem that cannot be overlooked, and this competition results in a considerable reduction in training speed. We propose a system deployed in cluster nodes that includes traffic monitoring, classification, and prediction of DDL training status and implement a priority-based traffic scheduling policy that considers DDL training features. We demonstrate that this policy is beneficial compared to Linux's default policy, which can increase training speed by approximately 18%. Additionally, we increase the training speed by approximately 20% in the case of continuous background traffic by increasing the bandwidth priority during DDL training. © 2022 IEEE.

关键词： Deep learning

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BTQoS: A Tenant Relationship-Aware QoS Framework for Multi-tenant distributed Storage System 8th

BTQoS: A Tenant Relationship-Aware QoS Framework for Multi-t...

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8th international Joint conference on Web and Big Data and Web-Age Information Management (APWeb-WAIM)

作者： Lu, Yixuan Qu, Yun Zhu, Dongjie Zhang, Rui Du, Haiwen Harbin Inst Technol Weihai Peoples R China

ISBN: (纸本)9789819772407;9789819772414

Quality of Service (QoS) is widely applied in multi-tenant distributed systems to meet tenants' Service Level Objective (SLO). Previous work ignored the relationship between tenants, making it challenging to achieve hierarchical resource management of tenants. this work proposes BTQoS, a tenant relationship-aware QoS framework for multi-tenant distributed storage systems. BTQoS introduces behavior and behavior tree abstractions to manage tenants' SLOs and relationships. BTQoS uses a two-level scheduling scheme to implement SLO enforcement, resource adaptation, and skew avoidance under dynamic workloads. In addition, BTQoS's two-level scheduler decouples tenant resource allocation and cluster resource adaptation, allowing the SLOs supported by BTQoS to be expanded with low development costs. We implement and evaluate BTQoS in HDFS. the experimental results show that BTQoS can hierarchically manage and enforce tenants' SLOs at low overheads and provide skew avoidance.

关键词： QoS Cloud computing distributed storage system

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