Abstract
Intelligent anomaly detection for identifying cyber/physical attacks to guarantee the work efficiency and safety is still a challenging issue, especially when dealing with few labeled data for cyber-physical security protection. In this study, we propose a few-shot learning model based on Siamese Convolution Neural Network (FS-SCNN), to alleviate the over-fitting issue and enhance the accuracy for intelligent anomaly detection in industrial CPSs. A Siamese CNN encoding network is constructed to measure distances of input samples based on their optimized feature representations. A robust cost function design including three specific losses is then proposed to enhance the efficiency of training process. An intelligent anomaly detection algorithm is developed finally. Experiment results based on a fully labeled public dataset and a few labeled dataset demonstrate that our proposed FS-SCNN can significantly improve FAR and F1 scores when detecting intrusion signals for industrial CPS security protection.
Original language | English |
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Journal | IEEE Transactions on Industrial Informatics |
DOIs | |
Publication status | Accepted/In press - 2020 |
Keywords
- Analytical models
- Anomaly Detection
- Anomaly detection
- CNN
- Feature extraction
- Few-Shot Learning
- Industrial CPS
- Object recognition
- Security
- Siamese Network
- Task analysis
- Training
ASJC Scopus subject areas
- Control and Systems Engineering
- Information Systems
- Computer Science Applications
- Electrical and Electronic Engineering