A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact.
Álvaro Huertas-GarcíaCarlos Martí-GonzálezRubén García MaezoAlejandro Echeverría ReyPublished in: CoRR (2023)
Keyphrases
- machine learning algorithms
- anomaly detection
- environmental impact
- benchmark data sets
- intrusion detection
- machine learning
- learning algorithm
- detecting anomalies
- anomalous behavior
- machine learning methods
- network intrusion detection
- learning problems
- decision trees
- intrusion detection system
- network traffic
- energy consumption
- machine learning models
- learning tasks
- one class support vector machines
- detect anomalies
- unsupervised learning
- real world
- network anomaly detection
- training data
- fuel consumption
- transfer learning
- negative selection algorithm
- supervised learning