Fine-tuned LSTM-Based Model for Efficient Honeypot-Based Network Intrusion Detection System in Smart Grid Networks
Author:
Affiliation:
1. College of Science and Engineering, Hamad Bin Khalifa University,Division of Information and Computing Technology,Doha,Qatar
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10018924/10018967/10019245.pdf?arnumber=10019245
Reference20 articles.
1. A Dynamic Recommendation-based Trust Scheme for the Smart Grid
2. A novel state estimation method for smart grid under consecutive denial of service attacks;li;IEEE Systems Journal,2022
3. An Intrusion Detection Model based on a Convolutional Neural Network
4. Analysis of Machine Learning Techniques Based Intrusion Detection Systems
5. Machine Learning and Deep Learning Approaches for CyberSecurity: A Review
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2. The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey;IEEE Internet of Things Journal;2024-05-01
3. Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks;IEEE Open Journal of Vehicular Technology;2024
4. Detection of cyber-attacks on smart grids using improved VGG19 deep neural network architecture and Aquila optimizer algorithm;Signal, Image and Video Processing;2023-11-17
5. Double-Edged Defense: Thwarting Cyber Attacks and Adversarial Machine Learning in IEC 60870-5-104 Smart Grids;IEEE Open Journal of the Industrial Electronics Society;2023
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