Visual Programmed IoT Beehive Monitoring for Decision Aid by Machine Learning based Anomaly Detection.
Rüdiger MachhamerJannik AltenhoferKristof UedingLevin CzenkuschFlorian StolzMaximilian HarthMichael MatternAzhar LatifSwen HaabJürgen HerrmannAnke SchmeinkKlaus-Uwe GollmerGuido DartmannPublished in: MECO (2020)
Keyphrases
- anomaly detection
- decision aid
- machine learning
- detect anomalies
- network anomaly detection
- multi criteria
- intrusion detection
- decision making
- detecting anomalies
- anomalous behavior
- network intrusion detection
- decision makers
- network traffic
- unsupervised learning
- multiple criteria
- intrusion detection system
- pattern recognition
- one class support vector machines
- computer vision
- data mining
- learning algorithm
- cumulative sum
- decision trees
- bicriteria
- active learning
- supervised learning
- text mining
- text classification
- data analysis
- reinforcement learning
- model selection