Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction.
Md. Alamin TalukderMd. Manowarul IslamMd. Ashraf UddinKhondokar Fida HasanSelina SharminSalem A. AlyamiMohammad Ali MoniPublished in: CoRR (2024)
Keyphrases
- network intrusion detection
- imbalanced data
- machine learning
- feature extraction
- feature selection
- class imbalance
- feature vectors
- feature set
- intrusion detection
- anomaly detection
- minority class
- support vector machine
- intrusion detection system
- base classifiers
- fraud detection
- class distribution
- linear regression
- decision trees
- network traffic
- pattern recognition
- sampling methods
- active learning
- ensemble learning
- random forest
- principal component analysis
- decision boundary
- ensemble methods
- image features
- ensemble classifier
- classification models
- machine learning algorithms
- image classification
- cost sensitive learning
- pattern classification
- support vector machine svm
- semi supervised learning
- high dimensionality
- text mining
- text classification
- machine learning methods
- feature space
- reinforcement learning
- dimensionality reduction
- knowledge discovery
- pairwise
- svm classifier
- random sampling
- concept drift
- supervised learning