Detection and classification of anomaly intrusion using hierarchy clustering and SVM.
Chenghua TangYang XiangYu WangJunyan QianBaohua QiangPublished in: Secur. Commun. Networks (2016)
Keyphrases
- anomaly detection
- support vector machine svm
- support vector
- support vector machine
- intrusion detection
- classification algorithm
- svm classifier
- classification method
- svm classification
- unsupervised learning
- feature vectors
- feature selection
- generalization ability
- classification accuracy
- training set
- feature space
- unsupervised clustering
- clustering method
- supervised classification
- pattern recognition
- improves the classification accuracy
- machine learning
- multi class classification
- high classification accuracy
- network security
- intrusion detection system
- support vector machine classifiers
- fuzzy support vector machine
- high dimensional data
- clustering algorithm
- knn
- text classification
- model selection
- network intrusion detection
- false positives
- unbalanced data
- multi class svm
- k means
- object detection
- decision boundary
- high dimensionality
- cost sensitive
- feature reduction
- k nearest neighbor
- decision trees
- training data
- detect anomalies
- hyperplane
- standard svm
- supervised learning
- decision forest
- self organizing maps
- multi class
- image classification
- fold cross validation
- text classifiers
- dimensionality reduction