A supervised energy monitoring-based machine learning approach for anomaly detection in a clean water supply system.
Andres Robles-DuraznoNaghmeh MoradpoorJames McWhinnieGordon RussellPublished in: Cyber Security (2018)
Keyphrases
- anomaly detection
- water supply
- machine learning
- unsupervised learning
- detect anomalies
- network anomaly detection
- supervised learning
- optimal design
- intrusion detection
- water resources
- learning algorithm
- detecting anomalies
- semi supervised
- anomalous behavior
- feature selection
- network intrusion detection
- network traffic
- behavior analysis
- support vector machine
- unsupervised anomaly detection
- pattern recognition
- data mining
- decision trees
- data analysis
- intrusion detection system
- computer vision
- databases
- detecting anomalous
- training data
- active learning
- natural language processing
- network security
- text classification
- reinforcement learning
- network intrusion
- computational intelligence
- one class support vector machines
- transfer learning
- model selection