Residual Convolutional Network for Detecting Attacks on Intrusion Detection Systems in Smart Grid
Author:
Affiliation:
1. University of North Dakota,School of Computer Science and Electrical Engineering,Grand Forks,ND,USA,58203
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9813697/9813750/09813983.pdf?arnumber=9813983
Reference24 articles.
1. Anomaly-Based Intrusion Detection From Network Flow Features Using Variational Autoencoder
2. $Deep-Full-Range$ : A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection Framework
3. Real-Time Network Intrusion Detection System Based on Deep Learning
4. Identification of malicious activities in industrial internet of things based on deep learning models
5. An Autoencoder and LSTM based Intrusion Detection approach against Denial of service attacks
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2. Analysis of Predictive Models for Revealing Household Characteristics using Smart Grid Data;2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE);2023-10-23
3. A White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-Based Cybersecurity Solution for a Smart City Environment;Sensors;2023-08-24
4. Unsupervised Network Traffic Anomaly Detection Method Based on Attribute Graph in Power Internet of Things;2023 3rd International Conference on Energy Engineering and Power Systems (EEPS);2023-07-28
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