Anomaly Detection From Low-Dimensional Latent Manifolds With Home Environmental Sensors.
Francisco Manuel Melgarejo-MeseguerAndrés Lorenzo BledaSergio Eduardo AbbenanteFrancisco Javier Gimeno-BlanesEstrella Everss-VillalbaSergio Muñoz-RomeroJosé Luis Rojo-ÁlvarezRafael Maestre-FerrizPublished in: IEEE Internet Things J. (2024)
Keyphrases
- anomaly detection
- low dimensional
- latent space
- high dimensional
- manifold learning
- high dimensional data
- euclidean space
- intrusion detection
- dimensionality reduction
- detecting anomalies
- low dimensional manifolds
- network intrusion detection
- principal component analysis
- data points
- anomalous behavior
- behavior analysis
- network traffic
- unsupervised anomaly detection
- detecting anomalous
- intrusion detection system
- computer security
- feature space
- lower dimensional
- latent variables
- sensor data
- unsupervised learning
- malware detection
- one class support vector machines
- network anomaly detection
- neural network
- embedding space
- network security
- detect anomalies
- training data
- probabilistic model
- active learning
- pattern recognition
- negative selection algorithm
- feature extraction
- learning algorithm