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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

共 149974 条记录，以下是781-790 订阅

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TGCN-DA: A Temporal Graph Convolutional Network with Data Augmentation for High Accuracy Insider Threat Detection 22

TGCN-DA: A Temporal Graph Convolutional Network with Data Au...

引用

ieee 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Li, Ximing Li, Linghui Li, Xiaoyong Cai, Binsi Li, Bingyu Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv Minist Educ Beijing Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Insider threats present a formidable challenge to cybersecurity, as insiders possess the privileges and information necessary to execute diverse attacks. A comprehensive analysis of user behavior, including behavioral features, sequences, and inter-user relationships, is required for effective insider threat detection. However, few existing methods consider these features in an integrated manner, which could result in high false positives. To further improve the accuracy of insider threat detection, we propose a novel framework for insider threat detection based on a temporal graph convolutional network with data augmentation (referred to as TGCN-DA), which integrates the exploration of structural information among users and simultaneously captures the behavior temporal dependencies. In particular, we introduce an edge predictor to encode user structural information and strengthen intra-class edges among users based on the representation of users' behavior. Additionally, the GCN with temporal feature mechanism is leveraged to learn dynamic changes in users' behavior to capture behavior temporal dependence. Extensive experiments demonstrate that our proposed TGCN-DA outperforms other state-of-the-art methods and achieves higher accuracy in the task of insider threat detection.

关键词： Insider Threat Detection Anomaly Detection Graph Neural Network

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COGNIT: Challenges and Vision for a Serverless and Multi-Provider Cognitive Cloud-Edge Continuum 7

COGNIT: Challenges and Vision for a Serverless and Multi-Pro...

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7th ieee international conference on Edge computing and Communications (ieee EDGE) / ieee World Congress on Services (SERVICES)

作者： Townend, Paul Marti, Alberto P. de la Iglesia, Idoia Matskins, Nikolaos Timoudas, Thomas Ohlson Hallmann, Torsten Lalaguna, Antonio Swat, Kaja Renzi, Francesco Bochenski, Dominik Mancini, Marco Bhuyan, Monowar Gonzalez-Hierro, Marco Dupont, Sebastien Kristiansson, Johan Montero, Ruben S. Elmroth, Erik Valdes, Ivan Massonet, Philippe Olsson, Daniel Llorente, Ignacio M. Ostberg, Per-Olov Abdou, Michael Umea Univ Dept Comp Sci Umea Sweden OpenNebula Syst Madrid Spain IKERLAN Arrasate Mondragon Spain Ctr Excellence Technol Informat & Commun CETIC Charleroi Belgium RISE Res Inst Sweden Digital Syst Div Comp Sci Gothenburg Sweden SUSE Nurnberg Germany Aeronaval Construcciones & Instalaciones ACISA Madrid Spain Phoenix Syst Warsaw Poland Nat 4 0 SB Srl Romano Di Lombardia Italy Univ Tuscia DIBAF Viterbo Italy Atende Ind Warsaw Poland CMCC Fdn Viterbo Italy Univ Complutense Madrid Madrid Spain

ISBN: (纸本)9798350304831

Use of the serverless paradigm in cloud application development is growing rapidly, primarily driven by its promise to free developers from the responsibility of provisioning, operating, and scaling the underlying infrastructure. However, modern cloud-edge infrastructures are characterized by large numbers of disparate providers, constrained resource devices, platform heterogeneity, infrastructural dynamicity, and the need to orchestrate geographically distributed nodes and devices over public networks. This presents significant management complexity that must be addressed if serverless technologies are to be used in production systems. This position paper introduces COGNIT, a major new European initiative aiming to integrate AI technology into cloud-edge management systems to create a Cognitive Cloud reference framework and associated tools for serverless computing at the edge. COGNIT aims to: 1) support an innovative new serverless paradigm for edge application management and enhanced digital sovereignty for users and developers;2) enable on-demand deployment of large-scale, highly distributed and self-adaptive serverless environments using existing cloud resources;3) optimize data placement according to changes in energy efficiency heuristics and application demands and behavior;4) enable secure and trusted execution of serverless runtimes. We identify and discuss seven research challenges related to the integration of serverless technologies with multi-provider Edge infrastructures and present our vision for how these challenges can be solved. We introduce a high-level view of our reference architecture for serverless cloud-edge continuum systems, and detail four motivating real-world use cases that will be used for validation, drawing from domains within Smart Cities, Agriculture and Environment, Energy, and Cybersecurity.

关键词： serverless FaaS edge computing resource management multi-provider cognitive cloud open source

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Fault-Tolerant Deep Learning Cache with Hash Ring for Load Balancing in HPC systems

Fault-Tolerant Deep Learning Cache with Hash Ring for Load B...

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2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Lee, Seoyeong Khan, Awais Kim, Yoochan Park, Junghwan Hwang, Soon Lee, Jae-Kook Hong, Taeyoung Zimmer, Christopher Kim, Youngjae Sogang University Korea Republic of Oak Ridge National Laboratory United States KISTI Korea Republic of

ISBN: (纸本)9798350355543

Large-scale DL on HPC systems like Frontier and Summit uses distributed node-local caching to address scalability and performance challenges. However, as these systems grow more complex, the risk of node failures increases, and current caching approaches lack fault tolerance, jeopardizing large-scale training jobs. We analyzed six months of SLURM job logs from Frontier and found that over 30% of jobs failed after an average of 75 minutes. To address this, we propose fault-tolerance strategies that recache data lost from failed nodes using a hash ring technique for balanced data recaching in the distributed node-local caching, reducing reliance on the PFS. Our extensive evaluations on Frontier showed that the hash ring-based recaching approach reduced training time by approximately 25% compared to the approach that redirects I/O to the PFS after node failures and demonstrated effective load balancing of training data across nodes. © 2024 ieee.

关键词： distributed Deep Learning Fault Tolerance HPC NVMe Cache

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Research on Fine-Grained Task Offloading in Mobile Edge computing 4

Research on Fine-Grained Task Offloading in Mobile Edge Comp...

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4th international conference on Information Science, Parallel and distributed systems, ISPDS 2023

作者： Wang, Yaping Zhao, Shuxu School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou730000 China

ISBN: (纸本)9798350337181

Edge computing is considered a promising architecture for handling latency-sensitive and computationally intensive tasks. The lack of consideration for the timing of jobs and their unique topology in the existing research on task scheduling in mobile edge computing settings results in performance deterioration and underutilization of edge servers. In this paper, we provide a two-tier, lightweight offloading mechanism. by carefully taking into account: 1) the job topology, 2) the task urgency, and 3) the rivalry for server resources. Simulation findings show that, as compared to baseline techniques, our proposed approach considerably increases application completion rates while reducing average system completion delay by 66.7% and 37.6% under various user and edge server counts. © 2023 ieee.

关键词： Mobile edge computing

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SmartDefend - IoT Security Using Machine Learning 8

SmartDefend - IoT Security Using Machine Learning

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8th ieee international conference on distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Saumya, Y.M. Vinay, P. Pinto, Charis Correia, Natasha Elizabeth Miranda, Melanie Crystal Dsouza, Joyline Rencita St Joseph Engineering College Dept. of CSE Vamanjoor India Canara Engineering College Dept. of CSE Bantwal India

ISBN: (纸本)9798350350593

With the advent of the fourth industrial revolution in recent years, technological advancements have led to massive exponential growth in the Internet of Things (loTs), fog computing, computer security, and cyberattacks. One of the most promising techniques for mitigating cybersecurity risks and ensuring security is machine learning, or ML. In order to address the current security and privacy issues, a number of research have suggested smart intrusion detection systems (IDS) using intelligent architectural frameworks that use AI. This paper presents a systematic literature review (SLR) that maps, surveys, and categorises the body of research on machine learning techniques for identifying cybersecurity threats in Internet of Things environments. Notably, random forests (RF) and support vector machines (SVM) are two of the most popular techniques because of their effective memory usage and excellent detection accuracy. The system aims not only to detect anomalies but to delve deeper, providing detailed insights through the precise classification of detected anomalies into specific attack types. Among the seven algorithms examined, the Random Forest Classifier yields the best accuracy of 92.72% in binary classification and 92.40% in multiclass classification. © 2024 ieee.

关键词： Fog computing

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0.13 μm SiGe HBT BiCMOS Charge Pump PLL for distributed Beamforming Applications 67

0.13 μm SiGe HBT BiCMOS Charge Pump PLL for Distributed Bea...

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67th ieee international Midwest Symposium on Circuits and systems (MWSCAS)

作者： Ahmad, Waleed Zakir, Subhan Al Seragi, Ebrahim Rajendra, Yashas L. Kiyaei, Alireza Zeinolabedinzadeh, Saeed Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85281 USA

ISBN: (纸本)9798350387186;9798350387179

This paper presents a type II charge pump PLL operating at 23.8 GHz in 0.13 mu m SiGe BiCMOS technology for distributed beamforming applications. The PLL includes a differential Colpitts VCO that generates a highly reliable phased-locked signal. It provides a good locking range of 3.5 GHz and a loop bandwidth of 1 MHz with a phase margin of 77 degrees. Additionally, at a 1 MHz offset, the PLL exhibits a measured phase noise of -94 dBc/Hz.

关键词： Type II PLL Phase Noise distributed Beamforming SiGe BiCMOS

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An Automated Framework for distributed Deep Learning-A Tool Demo 42

An Automated Framework for Distributed Deep Learning-A Tool ...

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42nd ieee international conference on distributed computing systems (ICDCS)

作者： Gharibi, Gharib Patel, Ravi Khan, Anissa Gilkalaye, Babak Poorebrahim Vepakomma, Praneeth Raskar, Ramesh Penrod, Steve Storm, Greg Das, Riddhiman TripleBlind Kansas City MO 64112 USA MIT Media Lab 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

Split learning (SL) is a distributed deep-learning approach that enables individual data owners to train a shared model over their joint data without exchanging it with one another. SL has been the subject of much research in recent years, leading to the development of several versions for facilitating distributed learning. However, the majority of this work mainly focuses on optimizing the training process while largely ignoring the design and implementation of practical tool support. To fill this gap, we present our automated software framework for training deep neural networks from decentralized data based on our extended version of SL, termed Blind Learning. Specifically, we shed light on the underlying optimization algorithm, explain the design and implementation details of our framework, and present our preliminary evaluation results. We demonstrate that Blind Learning is 65% more computationally efficient than SL and can produce better performing models. Moreover, we show that running the same job in our framework is at least 4.5 x faster than PySyft. Our goal is to spur the development of proper tool support for distributed deep learning.

关键词： Privacy-preserving machine learning distributed learning deep learning split learning

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Real-Time Sentiment Analysis Pipeline for Yelp Customer Feedback using ChatGPT’s LLM and distributed systems 6

Real-Time Sentiment Analysis Pipeline for Yelp Customer Feed...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Revathy, S. Shahid, J. Mohammad Salih, S. Computer Science and Engineering St.Joseph’s Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

This work proposes a real-time sentiment analysis pipeline on customer feedback using Yelp and addresses the high-volume dynamic user-generated contents processing problem. The proposal integrates state-of-the-art machine learning models and distributed systems, making use of Large Language Models within a Docker container called ChatGPT for the real-time sentiments analysis. The system ingests live Yelp data streams through TCP/IP, processes them in a scalable Apache Spark cluster, and ensures that data flows seamlessly through Apache Kafka. Enriched feedback is indexed into Elasticsearch, providing powerful analytics and visualizations through tools like Kibana, Tableau, and Power BI. The low-latency design of the pipeline gives enterprises actionable insights for real-time decision-making, enabling enhanced customer sentiment analysis and business intelligence. Scalable and efficient, this architecture fits all large volume Enterprise Feedback Management (EFM) needs when looking to achieve insights fast from customer sentiment. © 2025 ieee.

关键词： Sales

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N4: Network for N Neural Network Training 59

N4: Network for N Neural Network Training

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59th Annual ieee international conference on Communications (ieee ICC)

作者： Zhou, Jiasheng Lin, Shengrui Liu, Hongyan Chen, Xinyang Shi, Pengpai Zhu, Longlong Zhang, Dong Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China Fuzhou Univ Zhicheng Coll Fuzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9781728190549

As the amount of data and complexity of neural network models continue to grow, distributed training has become increasingly crucial for improving training speed. However, the bottleneck of distributed training is the communication overheads among distributed workers. Recent research has shown that performing in-network aggregation using programmable switches is a good way to accelerate distributed training. However, previous work has only targeted specific neural network models and can only be applied in specified network topologies. Administrators may train different models and train them in different network topologies. In order to generalize the approach of using programmable switches to accelerate distributed training, we propose N4, a programmable intra-switch acceleration framework that supports distributed training of multiple neural networks. N4 also realizes the deployment of distributed workers based on any topology. Our experimental results show that N4 ensures high performance and isolation when training numerous neural networks. N4 outperforms state-of-the-art systems, accelerating training for existing methods by up to 3.4x.

关键词： distributed training Programmable switches Greedy algorithms ILP

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Earthquake Detection System using IEC 61499 & ieee 1451 Standards

Earthquake Detection System using IEC 61499 & IEEE 1451 Stan...

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ieee international Instrumentation and Measurement Technology conference (I2MTC)

作者： Buonocore, Daniele Abrishambaf, Reza da Rocha, Helbert Paciello, Vincenzo Espirito-Santo, Antonio Univ Salerno Dept Ind Engn Fisciano Italy Miami Univ Dept Engn Technol Hamilton OH USA Univ Beira Interior Dept Electromech Engn IT Inst Telecommun Covilha Portugal

ISBN: (纸本)9798350380903;9798350380910

Earthquake Early Warning systems (EEWS) are developed to detect early signs of an earthquake, and using these systems, critical infrastructures can be shut down to prevent catastrophic disasters. This paper focuses on the design and development of an EEWS emphasizing control and instrumentation and highlighting the interoperability between IEC 61499 and ieee 1451 standards. The system is fully distributed from high to low levels, enabling robust implementation of a distributed control and measurement system.

关键词： IEC 61499 ieee 1451 Earthquake Detection SDG-9 (Industry innovation and infrastructure) SDG-11 (Sustainable cities and economies)

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