A Lightweight Unsupervised Learning Architecture to Enhance User Behavior Anomaly Detection.
André L. B. MolinaVinícius P. GonçalvesRafael Timóteo de Sousa Jr.Marcel PividalRodolfo Ipolito MeneguetteGeraldo P. Rocha FilhoPublished in: LATINCOM (2022)
Keyphrases
- lightweight
- anomaly detection
- user behavior
- unsupervised learning
- intrusion detection
- communication infrastructure
- supervised learning
- intrusion detection system
- detecting anomalies
- network traffic
- user preferences
- user interaction
- semi supervised
- anomalous behavior
- text classification
- dimensionality reduction
- expectation maximization
- network intrusion detection
- network anomaly detection
- machine learning
- behavior analysis
- object recognition
- user behavior patterns
- reinforcement learning
- network security
- model selection
- detect anomalies
- expert systems
- social networks
- one class support vector machines
- data mining