A Fully Unsupervised and Efficient Anomaly Detection Approach with Drift Detection Capability.
Chang How TanVincent C. S. LeeMahsa SalehiSlaven MarusicSrimal JayawardenaDion LuckePublished in: ICDM (Workshops) (2021)
Keyphrases
- anomaly detection
- intrusion detection
- fully unsupervised
- detecting anomalies
- drift detection
- anomalous behavior
- network intrusion detection
- intrusion detection system
- network traffic
- network anomaly detection
- one class support vector machines
- detect anomalies
- negative selection algorithm
- data sets
- active learning
- query processing
- machine learning