Improving Predictability of User-Affecting Metrics to Support Anomaly Detection in Cloud Services.
Vilc Queupe RufinoMateus Schulz NogueiraAlberto AvritzerDaniel Sadoc MenaschéBarbara RussoAndrea JanesVincenzo FermeAndré van HoornHenning SchulzCabral LimaPublished in: IEEE Access (2020)
Keyphrases
- anomaly detection
- intrusion detection
- end users
- detecting anomalies
- anomalous behavior
- network traffic
- network anomaly detection
- one class support vector machines
- intrusion detection system
- network intrusion detection
- database
- negative selection algorithm
- normal behavior
- databases
- detect anomalies
- user experience
- unsupervised learning
- data center
- cloud services
- smart card
- user interaction
- user interface
- data analysis
- feature selection
- cumulative sum