Optimizing IoT Intrusion Detection Using Balanced Class Distribution, Feature Selection, and Ensemble Machine Learning Techniques.
Muhammad Bisri MusthafaSamsul HudaYuta KoderaMd. Arshad AliShunsuke ArakiJedidah MwauraYasuyuki NogamiPublished in: Sensors (2024)
Keyphrases
- intrusion detection
- class distribution
- feature selection
- highly skewed
- imbalanced data
- base classifiers
- class imbalance
- training set
- training data
- machine learning
- intrusion detection system
- ensemble learning
- naive bayes
- multi class
- cost sensitive
- feature set
- anomaly detection
- network security
- ensemble methods
- network traffic
- test set
- network intrusion detection
- ensemble classifier
- concept drift
- unlabeled data
- text categorization
- machine learning methods
- detecting anomalous
- decision trees
- text classification
- test data
- data mining
- training samples
- feature space
- classification accuracy
- machine learning algorithms
- feature selection algorithms
- class labels
- supervised learning
- support vector machine
- dimensionality reduction
- feature subset
- model selection
- database systems
- training examples
- support vector
- text mining
- small number
- random forest
- knowledge discovery
- high dimensionality
- knn
- prediction accuracy