Anomaly detection for high-dimensional data using a novel autoencoder-support vector machine.
Zhuo JiangXiao HuangRongbin WangPublished in: J. Intell. Fuzzy Syst. (2023)
Keyphrases
- high dimensional data
- anomaly detection
- support vector machine
- nearest neighbor
- intrusion detection
- dimensionality reduction
- low dimensional
- data sets
- network intrusion detection
- subspace clustering
- high dimensional
- data analysis
- data points
- detecting anomalies
- support vector machine svm
- network traffic
- manifold learning
- svm classifier
- detect anomalies
- feature selection
- similarity search
- input space
- knn
- dimension reduction
- high dimensional spaces
- data distribution
- anomalous behavior
- feature space
- hyperplane
- lower dimensional
- clustering high dimensional data
- training data
- neural network
- unsupervised learning
- network anomaly detection
- kernel function
- support vector
- negative selection algorithm
- one class support vector machines
- high dimensional datasets
- training set
- intrusion detection system
- feature vectors
- input data
- kernel methods
- pairwise
- input image
- feature extraction
- image processing
- real world
- supervised learning