A Minimally Supervised Approach Based on Variational Autoencoders for Anomaly Detection in Autonomous Robots.
Davide AzzaliniLuca BonaliFrancesco AmigoniPublished in: IEEE Robotics Autom. Lett. (2021)
Keyphrases
- anomaly detection
- autonomous robots
- minimally supervised
- mobile robot
- intrusion detection
- detecting anomalies
- robotic systems
- network intrusion detection
- anomalous behavior
- lexical semantics
- network traffic
- intrusion detection system
- image segmentation
- one class support vector machines
- negative selection algorithm
- unsupervised learning
- generative model
- word sense disambiguation
- network anomaly detection
- artificial intelligence
- em algorithm
- natural language processing
- supervised learning
- feature vectors
- pairwise
- feature extraction
- detect anomalies
- clustering algorithm