STAD-FEBTE, a shallow and supervised framework for time series anomaly detection by automatic feature engineering, balancing, and tree-based ensembles: An industrial case study.
M. A. Zakeri HarandiChen LiCasper SchouSigurd L. VillumsenSimon BøghOle MadsenPublished in: AIM (2023)
Keyphrases
- anomaly detection
- intrusion detection
- detect anomalies
- detecting anomalies
- feature engineering
- intrusion detection system
- unsupervised learning
- network traffic
- anomalous behavior
- network intrusion detection
- negative selection algorithm
- network anomaly detection
- one class support vector machines
- knowledge discovery
- feature selection
- text classification
- knn
- hidden markov models
- data analysis
- bayesian networks
- similarity measure
- learning algorithm
- machine learning