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: J. Big Data (2024)
Keyphrases
- network intrusion detection
- imbalanced data
- machine learning
- feature extraction
- feature selection
- class imbalance
- feature vectors
- support vector machine
- intrusion detection
- feature set
- anomaly detection
- sampling methods
- fraud detection
- decision trees
- class distribution
- pattern recognition
- minority class
- network traffic
- intrusion detection system
- base classifiers
- active learning
- random forest
- ensemble methods
- cost sensitive
- machine learning methods
- feature subset
- classification models
- ensemble learning
- cost sensitive learning
- linear regression
- data sets
- svm classifier
- machine learning algorithms
- high dimensionality
- image classification
- text mining
- principal component analysis
- multi class
- knowledge discovery
- pattern classification
- k nearest neighbor
- text classification
- image features
- learning algorithm
- feature space
- data analysis
- data streams