Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection.
Muhammad Zaigham ZaheerJin-Ha LeeMarcella AstridArif MahmoodSeung-Ik LeePublished in: CoRR (2021)
Keyphrases
- anomaly detection
- minimally supervised
- label noise
- intrusion detection
- detecting anomalies
- clustering algorithm
- active learning
- network traffic
- network intrusion detection
- anomalous behavior
- detect anomalies
- intrusion detection system
- lexical semantics
- data points
- noise tolerant
- negative selection algorithm
- generative model
- unsupervised learning
- document clustering
- k means
- training data
- image processing