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Maintenance of Model Resilience in distributed Edge Learning Environments 19

Maintenance of Model Resilience in Distributed Edge Learning...

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19th international conference on Intelligent Environments, IE 2023

作者： Wang, Qiyuan Anagnostopoulos, Christos Fornes, Jordi Mateo Kolomvatsos, Kostas Vrachimis, Andreas University of Glasgow School of Computing Science United Kingdom University of Lleida Department of Computer Science Spain University of Thessaly Dept. of Informatics and Telecommunications Greece Glasgow University School of Computing Science United Kingdom

ISBN: (纸本)9798350312225

distributed Machine Learning (DML) at the edge of the network involves model learning and inference across networking nodes over distributed data. One type of model learning could be the delivery of predictive analytics services to formulate intelligent environments, however, those environments heavily rely on real-time inference and are significantly influenced by changes in the underlying data (concept drifts). Moreover, the quality of service and availability in DML environments are directly tied to each node's reliability, since such environments are highly susceptible to the impact of node failures. Even if such challenges can be tackled with distributed resilience mechanisms, their effectiveness and efficiency, due to concept drifts, should be maintained to ensure continuous and sustained quality of service. DML systems operate in dynamic environments, thus, they require their models to be updated according to the novel trends embedded in the new data they encounter. We, therefore, introduce several model maintenance mechanisms to ensure resilient DML systems in the long term when concept drifts emerge. We provide a comprehensive experimental evaluation of our resilience maintenance mechanisms over synthetic and real data showcasing their importance and applicability in edge learning environments. © 2023 IEEE.

关键词： Quality of service

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WideVision: A Low-Power, Multi-Protocol Wireless Vision Platform for distributed Surveillance 18

WideVision: A Low-Power, Multi-Protocol Wireless Vision Plat...

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18th international conference on Wireless and Mobile computing, networking and Communications (WiMob)

作者： Scherer, Moritz Sidler, Fabian Rogenmoser, Michael Magno, Michele Benini, Luca Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Zurich Switzerland Univ Bologna Dept Elect Elect & Informat Engn Bologna Italy

ISBN: (数字)9781665469753

ISBN: (纸本)9781665469753

The trend in Internet of Things research points toward performing increasingly compute-intensive data analysis tasks on embedded sensor nodes, rather than server centers. Exploiting the technological advances in both energy efficiency, and Tiny Machine Learning algorithms and methods, an increasing number of recognition and classification tasks can be performed by small, low-power, wireless sensor nodes. This paper presents WideVision, a wireless, wide-area sensing platform capable of performing on-board person detection with power requirements in the mW range. The WideVision platform integrates seamlessly into the Internet of Things, by coupling a dedicated multiradio platform, including a LoRa interface, enabling medium and long-range communication, with a novel parallel RISC-V microcontroller. We evaluate the proposed platform with the GAP8 microcontroller, which includes an 8-core RISC-V cluster, and greyscale camera to perform person detection by training and deploying an advanced, quantized neural network, achieving a statistical accuracy 84.5% for a 5-person detection task with a latency of only 182 ms. Experimental results demonstrate that the WideVision sensor node platform while performing inference at a rate of one image per minute on-board, is capable of lasting 300 days on a 2400 mAh Li-ion battery, and 65 days when evaluating one image per 10 seconds while providing effective surveillance of its perimeter.

关键词： Wireless communication Training Wireless sensor networks Power measurement Microcontrollers Surveillance Battery charge measurement

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Tero: Offloading CDN Traffic to Massively distributed Devices 24

Tero: Offloading CDN Traffic to Massively Distributed Device...

引用

25th international conference on distributed computing and networking, ICDCN 2024

作者： Vanerio, Juan Hügerich, Lily Schmid, Stefan University of Vienna Vienna Austria Tu Berlin Berlin Germany Fraunhofer Sit Darmstadt Germany

ISBN: (纸本)9798400716737

To provide high performance and cope with ever-increasing traffic demand, Content Delivery Network (CDN) providers have started considering the use of multi-tier architectures, including simple caching devices that can augment their server infrastructure, resulting in a massively distributed caching network. These caching devices are usually geographically distributed, although with limited storage space and bandwidth (e.g., set-top boxes), potentially alleviating the servers' load. This paper initiates the joint resource allocation and routing problem underlying such networks while providing at least a minimum bandwidth for each request. We present Tero, a system that maximizes throughput in such scenarios and leverages popularity forecasting to adapt to demand changes quickly. In Tero, the CDN's edge server decides whether to serve each request locally or redirect it to a specific caching device, maximizing overall system throughput by offloading traffic to the device caches. To adjust to the highly dynamic nature of the demand patterns, Tero performs frequent near-future content popularity predictions and makes allocation decisions every few minutes. We model the optimization problem under these constraints and derive optimality properties using a Lagrangian formulation from which we design heuristic algorithms. We evaluate Tero on a synthetic and a real-world large CDN request sequences, on ablation studies, and by comparing with an upper performance bound. Tero can reduce the edge server's throughput and provide sufficient bandwidth to each request, outperforming the competing baselines by up to 44% while being close to the performance of the ideal upper bounds. Also, Tero takes allocation decisions orders of magnitude faster than solving the exact problem. © 2024 Owner/Author.

关键词： Bandwidth

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DDoS Detection using Multilayer Perceptron 4

DDoS Detection using Multilayer Perceptron

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Christo, Mary Subaja Josepha Menandas, J. George, Mikhale Nuna, Sai Varun SRMIST Dept. of Networking and Communications Chennai India SRMIST School of Computing Dept of Networking and Communications Chennai India

ISBN: (纸本)9798350300093

In recent years, distributed denial of service (DDoS) attacks have grown to be a serious threat to network security, severely disrupting internet services and enterprises. Due to the dynamic and evolving nature of these attacks, detecting and mitigating them has become a difficult task. By examining the network traffic data, machine learning algorithms like Multilayer Perceptrons (MLPs) have demonstrated the potential in identifying DDoS attacks. This research study investigates the application of MLPs for DDoS detection and assess the model's performance on a real-world dataset. We also examine how various hyperparameters affect the model's performance and suggest an optimization technique to increase its accuracy. The outcomes of our research show that MLPs have the potential to be an effective tool for detecting and countering DDoS attacks, in addition to offering suggestions for future network security research © 2023 IEEE.

关键词： Denial-of-service attack

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An Ensemble Learning-Based DDoS Attack Detection Considering the Impact of ICA 14

An Ensemble Learning-Based DDoS Attack Detection Considering...

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14th international conference on computing Communication and networking Technologies, ICCCNT 2023

作者： Jana, Anique Mahato, Dipesh Karmakar, Raja Ghosal, Sandipan Chatterjee, Twinkle Techno International NewTown Computer Science and Engineering Kolkata700156 India Heritage Institute of Technology Computer Science and Engineering Kolkata700107 India Techno International NewTown Information Technology Kolkata700156 India

ISBN: (纸本)9798350335095

distributed denial of service (DDoS) attacks can cause considerable financial harm and operational concerns in the internet business era. Detecting such attacks necessitates overcoming two challenges: distinguishing normal packets from attack packets and consistently and effectively identifying distinct attack categories. As a result, an ideal detection system should be simple, reliable, and effective, with low false alarm rates. The goal of this paper is to give an analysis of machine learning (ML) methods for the detection of DDoS attacks using Ensemble Learning with and without implementing Independent Component Analysis (ICA). It improves the feature extraction, source separation, noise reduction, and data pre-processing, resulting in better performance and insights. The KDD-99 dataset is used to train and test ML models, using Logistic Regression, Random Forest, Decision Trees, K-Nearest Neighbors, and Gaussian Naive Bayes. They are used to extract data while categorizing the different types of input features during the training. The performance of the proposed mechanism is analyzed with and without the use of ICA. Results show that the DDoS attack is most successfully detected using the Random Forest classifier with an accuracy of 99.9670%. Moreover, ICA improves the performance of all ML models when compared to unsupervised learning techniques. © 2023 IEEE.

关键词： Decision trees

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Q-Learning-Based Task Allocation for High-Speed distributed computing in Heterogeneous Clusters

Q-Learning-Based Task Allocation for High-Speed Distributed ...

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international conference on Information networking

作者： Seung Geon Yang Girim Kwon Seung-Chan Lim School of ICT Robotics and Mechanical Engineering Hankyong National University Anseong South Korea School of Electrical and Computer Engineering University of Seoul Seoul South Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

This paper proposes a Q-learning-based task allocation approach for wireless coded distributed computing systems with heterogeneous worker nodes. Task allocation in such systems is challenging due to the heterogeneity in computation and communication capabilities, leading to non-identically and independently distributed processing times across nodes. By modeling the task allocation problem as a Markov decision process and applying Q-learning, the master node learns to allocate tasks effectively, adapting to node heterogeneity and minimizing the average processing time. This approach highlights the potential of reinforcement learning to optimize distributed computing in heterogeneous environments.

关键词： Wireless communication distributed processing Adaptation models Q-learning Markov decision processes Computational modeling Delays Resource management distributed computing Optimization

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Cybersecurity of distributed Systems and Dispersed computing 37th

Cybersecurity of Distributed Systems and Dispersed Computing

引用

37th international conference on Advanced Information networking and Applications, AINA 2023

作者： Ogiela, Urszula Takizawa, Makoto Ogiela, Lidia AGH University of Science and Technology 30 Mickiewicza Ave Kraków30-059 Poland Research Center for Computing and Multimedia Studies Hosei University 3-7-2 Kajino-cho Tokyo Koganei-shi184-8584 Japan

ISBN: (纸本)9783031284502

This paper will describe the most important aspects of cybersecurity systems oriented for distributed communication as well as dispersed computation. Especially the most important ways how to guarantee the highest level of security will be describes, and issues how to prevent cyberattacks. Especially human factors which have influence for general security levels also will be considered. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cybersecurity

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Enhancing the Performance of an Autonomous distributed Storage System in a Large-Scale Network 23

Enhancing the Performance of an Autonomous Distributed Stora...

引用

23rd ACIS international Summer Virtual conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed computing, SNPD-Summer 2022

作者： Nunome, Atsushi Hirata, Hiroaki Kyoto Institute of Technology Faculty of Information and Human Sciences Kyoto Japan

ISBN: (纸本)9798350396379

We are developing an autonomous distributed storage system that autonomously relocates data blocks according to their utilization and the effective performance of storage. This paper proposes an enhancement to the distributed storage system to address large network environments where many network nodes are connected to multiple network switches. In the proposed method, the latency time when a network frame travels on the network is evaluated together with the effective performance of the storage, and this is used to determine the destination of the data block. Simulation evaluation results show that in the networks with cascading multiple network switches and high I/O load on storage, the performance improvement is significantly greater than that of the previous method. This showed that our storage system can provide sufficient performance improvement even in large network environments. © 2022 IEEE.

关键词： Digital storage

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international Program Committee

International Program Committee

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Software Engineering, Artificial Intelligence, networking and Parallel/distributed computing (SNPD-Winter), ACIS international Winter conference on

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A Facial Authentication System Using Post-Quantum-Secure Data Generated on Mobile Devices 22

A Facial Authentication System Using Post-Quantum-Secure Dat...

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28th Annual international conference on Mobile computing and networking (ACM MobiCom)

作者： Lopez-Gonzalez, Paula Arjona, Rosario Roman, Roberto Baturone, Iluminada Univ Seville Inst Microelect Sevilla IMSE CNM CSIC Seville Spain

ISBN: (纸本)9781450391818

This paper describes a demonstrator of a post-quantum-secure facial authentication system distributed between a mobile device acting as a client and a remote computer acting as an authentication server. Homomorphic encryption based on Classic McEliece, one of the fourth-round candidates of the NIST post-quantum standardization process, is carried out by the client for protecting the biometric data extracted from the users' faces at enrollment and verification. The remote computer only stores and compares the received protected data, thus preserving user privacy. An Android App and a Graphical User Interface (GUI) were implemented at the client and the server, respectively, to show the system performance in terms of computation and security.

关键词： Biometric template protection homomorphic encryption post-quantum security distributed and mobile architectures

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