DAICS: A Deep Learning Solution for Anomaly Detection in Industrial Control Systems

Author:

Abdelaty Maged Fathy1,Doriguzzi Corin Roberto2,Siracusa Domenico3

Affiliation:

1. RiSING Research Unit, Fondazione Bruno Kessler, 18466 Trento, Trento, Italy, (e-mail: mabdelaty@fbk.eu)

2. Cybersecurity, Fondazione Bruno Kessler, 18466 Trento, Trento, Italy, 38123 (e-mail: rdoriguzzi@fbk.eu)

3. RiSING Research Unit, Fondazione Bruno Kessler, 18466 Trento, Trento, Italy, (e-mail: dsiracusa@fbk.eu)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Human-Computer Interaction,Information Systems,Computer Science (miscellaneous)

Cited by 23 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. HybridAD: A Hybrid Model-Driven Anomaly Detection Approach for Multivariate Time Series;IEEE Transactions on Emerging Topics in Computational Intelligence;2024-02

2. Packet Analysis and Information Theory on Attack Detection for Modbus TCP;IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society;2023-10-16

3. A Hybrid CNN-LSTM Framework for Unsupervised Anomaly Detection in Water Distribution Plant;2023 Innovations in Intelligent Systems and Applications Conference (ASYU);2023-10-11

4. Fast Attack Detection Method for Imbalanced Data in Industrial Cyber-Physical Systems;Journal of Artificial Intelligence and Soft Computing Research;2023-10-01

5. Differential evolution-based convolutional neural networks: An automatic architecture design method for intrusion detection in industrial control systems;Computers & Security;2023-09

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