☆ 4.8 Article

Deep Generative Models in the Industrial Internet of Things: A Survey

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Computer Science, Artificial Intelligence

Generative Adversarial Networks for Spatio-temporal Data: A Survey

Nan Gao et al.

Summary: This article provides a comprehensive review of the recent developments of GANs for spatio-temporal data, including the application of popular GAN architectures and the common practices for evaluating the performance of spatio-temporal applications. Future research directions are also pointed out.

ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY (2022)

添加到收藏夹

Article Computer Science, Information Systems

DIGFuPAS: Deceive IDS with GAN and function-preserving on adversarial samples in SDN-enabled networks

Phan The Duy et al.

Summary: Machine learning techniques are increasingly used in malicious network traffic detection to enhance the ability of intrusion detection systems (IDS). This study proposes the DIGFuPAS framework for generating adversarial attack samples to deceive IDS in SDN-enabled networks. Experimental results demonstrate that this framework can lead to misclassification of IDS on GAN-based synthetic attacks, reducing the detection rate of black-box IDSs.

COMPUTERS & SECURITY (2021)

添加到收藏夹

Article Automation & Control Systems

Nonlinear quality-related fault detection using combined deep variational information bottleneck and variational autoencoder

Peng Tang et al.

Summary: This paper proposes a novel deep VIB-VAE algorithm to extract quality-related and unrelated information, monitor faults with monitoring statistics, and improve performance with VAE reconstruction error.

ISA TRANSACTIONS (2021)

添加到收藏夹

Article Automation & Control Systems

Deep-IFS: Intrusion Detection Approach for Industrial Internet of Things Traffic in Fog Environment

Mohamed Abdel-Basset et al.

Summary: This article presents a forensics-based deep learning model, Deep-IFS, for intrusion detection in IIoT traffic. By utilizing local gated recurrent unit and multihead attention layers to learn local and global representations, and deploying and training the model in a fog computing environment, it effectively handles large-scale IIoT traffic data and achieves good distributed processing results.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

添加到收藏夹

Article Automation & Control Systems

Network Traffic Prediction in Industrial Internet of Things Backbone Networks: A Multitask Learning Mechanism

Laisen Nie et al.

Summary: This article investigates the issues of IIoT-oriented backbone network traffic prediction and proposes an effective prediction mechanism using multitask learning (MTL). A deep learning architecture constructed by MTL and long short-term memory is designed to improve prediction accuracy. The effectiveness is evaluated by implementing the mechanism on real network.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

添加到收藏夹

Article Computer Science, Theory & Methods

A Survey on Trajectory Data Management, Analytics, and Learning

Sheng Wang et al.

Summary: This survey comprehensively reviews recent research trends in trajectory data management, covering various aspects such as trajectory pre-processing, storage, common trajectory analytic tools, and related analytical tasks. The study also delves into deep trajectory learning and outlines essential qualities that trajectory data management systems should possess for maximum flexibility.

ACM COMPUTING SURVEYS (2021)

添加到收藏夹

Article Automation & Control Systems

An Adaptive Trust Boundary Protection for IIoT Networks Using Deep-Learning Feature-Extraction-Based Semisupervised Model

Mohammad Mehedi Hassan et al.

Summary: The rapid development of IoT platforms in the industrial domain has brought critical solutions, but also exposed industrial systems to cyber risks. An adaptive trust boundary protection approach for IIoT networks, utilizing deep learning and feature extraction, has been proposed and shown to significantly improve attack identification.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

添加到收藏夹

Article Automation & Control Systems

Challenges and Opportunities in Securing the Industrial Internet of Things

Martin Serror et al.

Summary: The Industrial Internet of Things, also known as Industry 4.0, is a natural trend towards interconnecting devices in industrial settings, similar to the success seen in consumer IoT. Although it brings many benefits, it also introduces serious security challenges. Unlike consumer IoT, securing the Industrial IoT focuses more on safety and productivity requirements.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

添加到收藏夹

Article Computer Science, Information Systems

Anomaly Detection Based on Multidimensional Data Processing for Protecting Vital Devices in 6G-Enabled Massive IIoT

Guangjie Han et al.

Summary: This study aims to address the fragmentation and outliers issues in Industrial Internet of Things by developing a method driven by sixth-generation networking. Utilizing multidimensional data relationship diagram and autoregressive exogenous model to quantify information for protecting high priority nodes, identifying high-value sensing devices, and enabling massive Internet of Things with characteristic patterns hidden in the data.

IEEE INTERNET OF THINGS JOURNAL (2021)

添加到收藏夹

Review Computer Science, Information Systems

Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm

Thanasis Kotsiopoulos et al.

Summary: Industry 4.0 is the new industrial revolution that utilizes artificial intelligence technologies such as machine learning and deep learning to evaluate manufacturing data and provide valuable insights. This paper highlights the principles of Industry 4.0, introduces a new architecture for AIA, and presents important ML and DL algorithms used in Industry 4.0.

COMPUTER SCIENCE REVIEW (2021)

添加到收藏夹

Article Computer Science, Hardware & Architecture

Generative adversarial networks based remaining useful life estimation for IIoT

Sourajit Behera et al.

Summary: This paper proposes a novel prognostics framework based on CGAN and DGRU network, which can generate multi-variate fault instances, solve data imbalance, and predict the RUL of complex systems with the least latency. Experimental results show that by using data augmentation and training DGRU, the RUL prediction accuracy has improved by at least 15% compared to reported imbalanced work.

COMPUTERS & ELECTRICAL ENGINEERING (2021)

添加到收藏夹

Article Computer Science, Hardware & Architecture

MACHINE LEARNING FOR MASSIVE INDUSTRIAL INTERNET OF THINGS

Hui Zhou et al.

Summary: The Industrial Internet of Things (IIoT) revolutionizes future manufacturing facilities by integrating IoT technologies, but faces unique challenges in dealing with massive IIoT problems. This article systematically summarizes the QoS requirements, unique characteristics, and machine learning solutions for massive IIoT scenarios.

IEEE WIRELESS COMMUNICATIONS (2021)

添加到收藏夹

Article Computer Science, Information Systems

Differential Privacy for Industrial Internet of Things: Opportunities, Applications, and Challenges

Bin Jiang et al.

Summary: The development of IoT has brought new changes and IIoT is promoting a new industrial revolution. With more IIoT applications, privacy protection issues are emerging. Differential privacy is used to protect user-terminal privacy, requiring in-depth research.

IEEE INTERNET OF THINGS JOURNAL (2021)

添加到收藏夹

Article Computer Science, Information Systems

A Robust Deep-Learning-Enabled Trust-Boundary Protection for Adversarial Industrial IoT Environment

Mohammad Mehedi Hassan et al.

Summary: The article addresses the challenging problem of trust-boundary protection in Industrial Internet of Things environments and proposes a cooperative data generator based on a downsampler-encoder to better capture the distribution of attack models. Experimental results demonstrate that this approach outperforms conventional deep learning and other ML techniques in terms of robustness against adversarial/noisy examples in the IIoT environment.

IEEE INTERNET OF THINGS JOURNAL (2021)

添加到收藏夹

Review Computer Science, Information Systems

Deep Learning in the Industrial Internet of Things: Potentials, Challenges, and Emerging Applications

Ruhul Amin Khalil et al.

Summary: Recent advances in IoT have led to a proliferation of interconnected devices and the use of various smart applications, with the application of deep learning algorithms in IIoT providing various new applications. This article introduces various DL techniques and their applications in different industries, as well as use cases and research challenges in smart manufacturing, smart metering, and smart agriculture in IIoT systems.

IEEE INTERNET OF THINGS JOURNAL (2021)

添加到收藏夹

Article Computer Science, Information Systems

Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis

Yasmin Fathy et al.

Summary: The Internet of Things is transforming manufacturing into Smart Manufacturing, which utilizes IoT data and machine learning to automate fault prediction and improve product quality. Imbalanced data hinders the success of machine learning in predicting faults.

IEEE ACCESS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities

Guo-Jun Qi

INTERNATIONAL JOURNAL OF COMPUTER VISION (2020)

添加到收藏夹

Article Computer Science, Hardware & Architecture

Industrial internet of things: Recent advances, enabling technologies and open challenges

W. Z. Khan et al.

COMPUTERS & ELECTRICAL ENGINEERING (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

GAN-Powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

Yuxiu Hua et al.

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS (2020)

添加到收藏夹

Article Automation & Control Systems

Neural Network-Based Model Predictive Control of a Paste Thickener Over an Industrial Internet Platform

Felipe Nunez et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

添加到收藏夹

Article Automation & Control Systems

Potential, challenges and future directions for deep learning in prognostics and health management applications

Olga Fink et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2020)

添加到收藏夹

Article Automation & Control Systems

LSTM Learning With Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT

Di Wu et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

添加到收藏夹

Article Engineering, Multidisciplinary

Intelligent fault diagnosis of rotating machinery via wavelet transform, generative adversarial nets and convolutional neural network

Pengfei Liang et al.

MEASUREMENT (2020)

添加到收藏夹

Article Computer Science, Hardware & Architecture

Generative Adversarial Networks

Ian Goodfellow et al.

COMMUNICATIONS OF THE ACM (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

Detection of Low-Frequency and Multi-Stage Attacks in Industrial Internet of Things

Xinghua Li et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)

添加到收藏夹

Article Chemistry, Analytical

ARIES: A Novel Multivariate Intrusion Detection System for Smart Grid

Panagiotis Radoglou Grammatikis et al.

SENSORS (2020)

添加到收藏夹

Article Computer Science, Information Systems

A comprehensive survey and analysis of generative models in machine learning

G. M. Harshvardhan et al.

COMPUTER SCIENCE REVIEW (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities

Yang Yang et al.

IEEE COMMUNICATIONS MAGAZINE (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Federated Machine Learning: Concept and Applications

Qiang Yang et al.

ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY (2019)

添加到收藏夹

Article Computer Science, Information Systems

Motor Fault Detection and Feature Extraction Using RNN-Based Variational Autoencoder

Yang Huang et al.

IEEE ACCESS (2019)

添加到收藏夹

Article Computer Science, Information Systems

Recent Progress on Generative Adversarial Networks (GANs): A Survey

Zhaoqing Pan et al.

IEEE ACCESS (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition

Zirui Wang et al.

NEUROCOMPUTING (2018)

添加到收藏夹

Article Chemistry, Analytical

Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT

Manuel Lopez-Martin et al.

SENSORS (2017)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.