Evaluating Machine Learning Approaches for Cyber and Physical Anomalies in SCADA Systems
Author:
Affiliation:
1. University Campus Bio-Medico di Roma,Unit of Automatic Control,Rome,Italy,00128
2. School of Innovation, Design and Engineering, Mälardalen University,Eskilstuna,Sweden,63220
Funder
INAIL
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10224900/10224823/10224915.pdf?arnumber=10224915
Reference23 articles.
1. Deep Learning-Based Cyber–Physical Feature Fusion for Anomaly Detection in Industrial Control Systems
2. Evaluation of Machine Learning Algorithms for Anomaly Detection in Industrial Networks
3. Distribution-balanced stratified cross-validation for accuracy estimation
4. KNN Model-Based Approach in Classification
5. Behaviour-Based Attack Detection and Classification in Cyber Physical Systems Using Machine Learning
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1. Holistic Risk Assessment in Industrial Control Systems: Combining Multiple Bayesian Networks with Multi-Criteria Decision Making;2024 32nd Mediterranean Conference on Control and Automation (MED);2024-06-11
2. Securing Cyber-Physical Systems with Two-level Anomaly Detection Strategy;2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS);2024-05-12
3. Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid;Energies;2024-04-20
4. A Two-Level Fusion Framework for Cyber-Physical Anomaly Detection;IEEE Transactions on Industrial Cyber-Physical Systems;2024
5. Attack Target Detection Using Machine Learning on SCADA Gas Pipeline Data;2023 International Conference on Computational Science and Computational Intelligence (CSCI);2023-12-13
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