Feed-Forward Intrusion Detection and Classification on a Smart Grid Network
Author:
Affiliation:
1. East Tennessee State University,Department of Computing,Johnson City,United States
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9720724/9720434/09720898.pdf?arnumber=9720898
Reference21 articles.
1. User Behavior Analytics for Anomaly Detection Using LSTM Autoencoder - Insider Threat Detection
2. Deep Learning Based Multi-Channel Intelligent Attack Detection for Data Security
3. EdgeLSTM: Towards Deep and Sequential Edge Computing for IoT Applications
4. BAT: Deep Learning Methods on Network Intrusion Detection using NSL-KDD dataset;tongtong;IEEE Access,0
5. A Novel Intrusion Detector Based on Deep Learning Hybrid Methods
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1. Cyberattack Classification in Smart Grid Distribution Substations using a Novel Ensemble Bagging Learning Technique;2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE);2024-08-06
2. A Comprehensive Review of Insider Threats and Social Engineering Attacks Detection: Challenges, Gaps, and a Deep Learning-Based Solution;2024
3. Analysis of Securing Edge-Cloud Computing and Network Based Deep Neural Intrusion Detection System as a Solution Model;Lecture Notes in Networks and Systems;2024
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