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检索条件"任意字段=Conference on Internet Quality and Performance and Control of Network Systems"

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An Unsupervised Convolutional Adversarial Anomaly Detection Model for IoT Data Infrastructure 24

An Unsupervised Convolutional Adversarial Anomaly Detection ...

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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： Wen, Peian Yang, Zhenyu Wu, Lei Qi, Sibo Chen, Juan School of Computer and Software Engineering Xihua University Chengdu China School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350319934

Anomaly detection is critical to ensure the IoT (internet of Things) data infrastructures' quality of Service. However, due to the complexity of incon-spicuous(indistinct) anomalies, high dynamicity, and lack of anomaly labels in the operational IoT systems and cloud infrastructures, multivariate time series anomaly detection becomes more difficult. Existing approaches are rarely effective in meeting these challenges. In this paper, We propose a novel convolutional adversarial model based on CNN adversarial training with POT (Peak Over Threshold) dynamic threshold selection (CAT-POT) for multivariate time series anomaly detection in the IoT data. We build on the original autoencoder, replacing the fundamental linear changes with a convolutional neural network and adding a convolutional decoder to make an adversarial training architecture. The ability of the model to identify 'slight' anomalies is enhanced through adversarial training, combined with POT dynamically selected thresholds to detect anomalies. We conduct ex-periments on five subsets of various open datasets, and the overall performance of CAT-POT is better than that of the baseline method. The average F1 value of CAT-POT on all datasets is 0.889, which is 5.1% higher than the suboptimal model, with the best improvement reaching 21.5%. © 2022 IEEE.

关键词： internet of things

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VPN network Traffic Classification Using Entropy Estimation and Time-Related Features 5th

VPN Network Traffic Classification Using Entropy Estimation ...

引用

5th International conference on Information and Communication Technology for Intelligent systems, ICTIS 2021

作者： Balachandran, Aswathi Amritha, P.P. TIFAC CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789811639449

The classification of internet traffic is gaining prominence for the past few years due to the widening of the current internet network and web-based applications. Many different approaches are being practiced based on the numerous studies conducted so far. The newer methodology can become imperative for service providers to offer a better quality of service to the users. Deep packet inspection is one such methodology used in the past for traffic characterization where network traffic is subsequently classified into different classes, but their accuracy has been declined due to the drastic changes in the internet traffic, particularly the increase in encrypted traffic. Virtual private networks (VPNs) have become a preferred choice among the users for remote access communication over other public internet or IP-based networks. In this paper, our proposed model is a combined approach of entropy estimation and machine learning algorithms, especially deep learning for the network traffic classification. We have used the time-related features to classify VPN or non-VPN network traffic and characterize encrypted traffic into different categories and application identification. We used random forest, KNN, and ANN to test the accuracy of our model. Our results show a good accuracy rate and performance and thus proved our proposed model with entropy as an additional feature, achieving accuracy levels above 90% for all the three algorithms to characterize VPN or non-VPN traffic and application identification. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Entropy

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A Multi-Attention Mechanisms Based Transfer Learning Framework for State of Health Estimation of Lithium-ion Battery 4

A Multi-Attention Mechanisms Based Transfer Learning Framewo...

引用

4th International conference on Smart Power and internet Energy systems (SPIES)

作者： Lu, Dong Cui, Naxin Li, Changlong Shandong Univ Sch Control Sci & Engn Jinan Peoples R China

ISBN: (纸本)9781665489577

Accurate state of health (SOH) estimation is essential for ensuring the stable and safe operation of lithium-ion batteries. However, the adaptability of the estimation method for batteries with different formulations remains challenging. In this paper, partial charging segments are collected and processed. A pre-training convolutional neural network (CNN), which combines attention mechanisms for heightening the estimation performance, is proposed for integrating the inputs and extracting the hidden features automatically. Experiments are performed to show that the proposed method could reduce the estimation error by 80.9%, 41.3% and 25.6% for LCO, LFP and NCA respectively. Moreover, to reduce the computation burden between different kinds of batteries, a transfer learning (TL) strategy is utilized by fine-tuning the dense layers. The transfer learning results show that the estimation root mean square error (RMSE) of LFP and NCA are only 1.3% and 2.6%, respectively.

关键词： Lithium-ion battery state of health (SOH) partial charging segments convolutional neural network transfer learning

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Solar Tree based Smart City Street Light control System using IoT BLYNK Platform 1

Solar Tree based Smart City Street Light Control System usin...

引用

1st IEEE International conference on Automation, Computing and Renewable systems, ICACRS 2022

作者： Dhanasekaran, S. Gomathi, P. Renaldo maximus, A. Krishnan, Thiyagarajan Kannan, B. Dept of ECE Sri Eshwar College of Engineering Coimbatore India Dept of ECE Selvam College of Technology Namakkal India Dept of ECE Government College of Engineering Tirunelveli India Dept of ECE PSG College of Technology Coimbatore India Dept of ECE Ramco Institute of Technology Rajapalayam India

ISBN: (纸本)9781665460842

Street lighting uses a lot of electricity around the world. Street lights in most cities are only managed regularly and the quality of service is very low. The worst levels will be caused by constraints on available resources, very limited equipment instrumentation, inappropriate system operation, and poor network maintenance. The goal of the current research is to build and develop a highly IoT-dependent smart city based on ESP8266. Its main goal is to create a subway IoT system that contributes to the development of smart cities and uses online cloud computing to solve real-time problems. Thanks to the internet of Things, street lighting and water management are now two factors that are automatically managed in smart cities and rural areas. In our current work, we tend to focus more on using internet of Things (IoT) devices to remotely control street lighting. IoT will allow access to real-time street lighting data (on/off status) anytime, anywhere. IoT is nothing more than a network that uses software, sensors, and connectivity to control and monitor data anytime, anywhere. The central office can conduct observations from anywhere. Data that is continuously stored in the cloud via BLYNK local servers and efficient client-server connections is visible to us in real time. Such automated systems will work more efficiently than manual systems, saving manpower and increasing the accuracy of the system. Electricity prices continue to rise as more and more energy is wasted. Saving energy is now absolutely necessary. To improve efficiency, a technology called street lighting monitoring to automatically control street lighting was created. The main reason for the work is to save energy. © 2022 IEEE

关键词： internet of things

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network Intrusion Detection System in a Light Bulb 32

Network Intrusion Detection System in a Light Bulb

引用

32nd International Telecommunication networks and Applications conference (ITNAC)

作者： Manocchio, Liam Daly Layeghy, Siamak Portmann, Marius Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia

ISBN: (纸本)9781665471039

internet of Things (IoT) devices are progressively being utilised in a variety of edge applications to monitor and control home and industry infrastructure. Due to the limited compute and energy resources, active security protections are usually minimal in many IoT devices. This has created a critical security challenge that has attracted researchers' attention in the field of network security. Despite a large number of proposed network Intrusion Detection systems (NIDSs), there is limited research into practical IoT implementations, and to the best of our knowledge, no edge-based NIDS has been demonstrated to operate on common low-power chipsets found in the majority of IoT devices, such as the ESP8266. This research aims to address this gap by pushing the boundaries on low-power Machine Learning (ML) based NIDSs. We propose and develop an efficient and low-power ML-based NIDS, and demonstrate its applicability for IoT edge applications by running it on a typical smart light bulb. We also evaluate our system against other proposed edge-based NIDSs and show that our model has a higher detection performance, and is significantly faster and smaller, and therefore more applicable to a wider range of IoT edge devices.

关键词： network Intrusion Detection System (NIDS) Machine Learning (ML) internet of Things (IoT) Edge Computing ESP32 WROOM

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Data-Driven Physics Based Ultra-short-term Wind Power Prediction Model: A VMD-LSTM and WRF Based Approach 4

Data-Driven Physics Based Ultra-short-term Wind Power Predic...

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4th International conference on Smart Power and internet Energy systems (SPIES)

作者： Cui, Xiyou Li, Hongwei Pan, Zhiyuan Zhang, Zhengmao State Grid China Technol Coll Jinan Peoples R China

ISBN: (纸本)9781665489577

In this paper, an ultra-short-term wind power prediction model is proposed. First, based on the Weather Research and Forecast (WRF) model, the predicted wind speed of the wind power plant is obtained. Second, extract log history data from wind farm data acquisition and supervisory control system (SCADA). After, a variational mode decomposition (VMD) is applied to process historical wind speed of the wind farm to create 5 training data sets. Finally, Using long short-term memory (LSTM) network as a predictive model. The historical wind speed, historical wind power and WRF predicted wind speed are used as inputs to output the predicted power for the next 4 hours. For evaluating the prediction accuracy of this model, considering root mean square error (RMSE) and mean absolute error (MAE) as error evaluation metrics, the proposed model is compared with 3 other algorithms. The results show that LSTM has the best prediction performance.

关键词： wind power prediction ultra-short-term long and short-term memory network WRF model

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control and Management of network Service quality Based on SRV6

Control and Management of Network Service Quality Based on S...

引用

International conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Xiao Yang Wang Taoran Liu Pinle School of Information and SoftWare Engineering University of Electronic Science and Technology of China Chengdu China

With the development of internet and communication technology, quality of Service has become an important indicator to measure and manage network performance, in order to meet the needs of diversified network applications, solve the problem of network congestion and meet the needs of users. At the same time, SRv6, as an extended protocol based on IPv6, provides the possibility of innovation and development for the network architecture through flexible routing and service customization. P4, as a data plane programming language that has emerged in recent years, has excellent programmability and adaptability for designing high-performance network systems. In depth study of QoS and SRv6 will provide new ideas and solutions for network optimization, user experience improvement and network sustainable development.

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H2IOSC Project: The Italian Federated Cluster for IoT-based Monitoring and Digital Twinning of Cultural Heritage 9

H2IOSC Project: The Italian Federated Cluster for IoT-based ...

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9th International conference on Smart and Sustainable Technologies (SpliTech)

作者： Bucciero, Alberto Chirivi, Alessandra Colella, Riccardo Emara, Mohamed Greco, Matteo Palama, Daniela Maria Pandurino, Andrea Taurino, Francesco Zecca, Davide ISPC CNR Lecce Italy

ISBN: (纸本)9798350390797;9789532901351

The H2IOSC project aims to create a federated cluster of research infrastructures (RIs) in the domain of Cultural Heritage at the national level in Italy. Through four key Ris - DARIAH- IT, CLARIN, OPERAS, and E-RIHS the project enables collaboration among researchers with interdisciplinary expertise. Within this context, DIGILAB emerges as a digital access platform for the Italian node of E-RIHS, providing data management, digital tools, and services to boost cultural heritage preservation, fruition, and study. A significant aspect of DIGILAB architecture is its capability to support geo-localization and real-time monitoring of cultural heritage sites in terms of environmental conditions, structural integrity, and diagnostics, leveraging on novel internet of Things (IoT) systems and large-scale Wireless Sensor networks (WSNs). By integrating WSNs into DIGILAB framework, the project enhances remote monitoring and control of cultural sites. These networks facilitate the collection of real-time data on factors such as temperature, humidity, and air quality, providing crucial insights for the Cultural Heritage research community. Moreover, WSNs enable proactive measures to be taken in response to emerging threats, mitigating risks and minimizing damage to cultural assets at a national level.

关键词： IoT cultural heritage wireless sensor network data processing data management data visualization

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Cluster routing algorithm based on agglomerative hierarchical clustering in wireless sensor networks

Cluster routing algorithm based on agglomerative hierarchica...

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2023 International conference on Intelligent systems, Communications, and Computer networks, ISCCN 2023

作者： Li, Hongbing Cui, Hao Ou, Jun Wang, Yuning Key Laboratory of Intelligent Information Processing Control of Chongqing Three Gorges University China Chongqing Key Laboratory of Geological Environment Monitoring Disaster Early Warning in Three Gorges Reservoir Area China Internet of Things and Intelligent Control Technology Chongqing Engineering Research Center China

ISBN: (纸本)9781510666238

To save the energy and extend the lifetime of the network, Cluster Routing Algorithm based on Agglomerative Hierarchical Clustering (CRAAHC) is proposed in wireless sensor networks. WSN is divided into a certain quantity of ring areas based on the energy consumption model. Clustering process is optimized by constraining the distance between clusters and the diameter of clusters to improve the quality of clustering algorithm. Cluster head selection strategy is improved by considering the residual energy of the node and energy consumption in communication with other nodes in one cluster. Simulation results show that CRAAHC algorithm can achieve energy efficiency and routing reliability. © 2023 SPIE.

关键词： Clustering algorithms

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IEEE lnternational conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2024

IEEE lnternational Conference on Advanced Trends in Radioele...

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IEEE lnternational conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2024

ISBN: (纸本)9783031612206

The proceedings contain 43 papers. The special focus in this conference is on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering. The topics include: AI Affected Job Replacements and Accompanying Ethical Problems;Generative AI-Language Models in Didactics and Communication for Inclusiveness;How AI Meets networking and networks Meet AI Applications;a Survey of Deep Learning for Remote Sensing, Earth Intelligence and Decision Making;intelligent Hierarchical Coordination Fault-Tolerant Routing Method Under End-to-End quality of Service Protection in Multidomain Softwarized networks;peculiarities of Classification of Lossy Compressed Multichannel Remote Sensing Images Using Trained Neural networks;Reducing the Impact of Unstable Connections Among Nodes of Wireless IIoT Clusters Using Machine Learning Methods;ensemble Methods of Determining the Effective Activity of Enterprises;The Energy Transition in Germany Requires an AI-Supported Dynamic control of the Power Supply network;Artificial Intelligence and MicroRNA: Role in Cancer Evolution;A Hybrid Collaborative Filtering Based Recommender Model Using Modified Funk SVD Algorithm;the Use of Artificial Intelligence Models in the Automated Knowledge Assessment System;development of a Universal High-performance Machine Learning Framework for Finding Cybersecurity Anomalies in Big Data;method for Detecting Phishing Sites;using of Computer Vision with Stroboscopic Imaging in 5G;fuzzy Logic Models for Technological and Communication Electronic control systems;membership Root-Polynomial Functions;computational Intelligence for IoT and Smart Home Based on Piezo-Electric Sensors and Fuzzy Logic;AI-Driven E2E Testing and Cucumber Test Generation: A GPT-Powered Approach for Improved Software quality and Collaboration;OrgPad Tool and Its Role for Elementary School STEM Didactics.

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