Network backbone anomaly detection using double random forests based on non-extensive entropy feature extraction.
Meijuan YinDong YaoJunyong LuoXiaonan LiuJing MaPublished in: ICNC (2013)
Keyphrases
- anomaly detection
- random forests
- network traffic
- network anomaly detection
- feature extraction
- detect anomalies
- intrusion detection
- network intrusion
- random forest
- detecting anomalies
- network intrusion detection
- decision trees
- ensemble methods
- normal behavior
- anomalous behavior
- logistic regression
- machine learning algorithms
- network security
- intrusion prevention
- intrusion detection system
- unsupervised learning
- one class support vector machines
- real world
- image classification
- artificial neural networks
- negative selection algorithm
- decision tree ensembles
- support vector machine svm
- feature set
- dimensionality reduction
- support vector
- image processing
- feature selection
- data mining