Hybrid Deep Learning-based Intrusion Detection System for Industrial Internet of Things

Author:

Khacha Amina1,Saadouni Rafika1,Harbi Yasmine2,Aliouat Zibouda2

Affiliation:

1. Ferhat Abbas University of Setif-1,Faculty of Sciences,Setif,Algeria

2. LRSD Laboratory, Ferhat Abbas University of Setif-1,Setif,Algeria

Publisher

IEEE

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep Learning for Enhanced IoMT Security: A GNN-BiLSTM Intrusion Detection System;2024 International Conference on Circuit, Systems and Communication (ICCSC);2024-06-28

2. Comparative Analysis of Cybersecurity Datasets in Industrial Control Systems;2024 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0 & IoT);2024-05-29

3. Optimising Intrusion Detection Through Long Short-Term Memory Model;2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE);2024-05-16

4. Securing the Industrial IoT: A Novel Network Intrusion Detection Models;2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT);2024-05-03

5. Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models;Mathematics;2024-02-14

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