A Novel Detection and Multi-Classification Approach for IoT-Malware Using Random Forest Voting of Fine-Tuning Convolutional Neural Networks.
Safa Ben AtitallahMaha DrissIman M. AlmomaniPublished in: Sensors (2022)
Keyphrases
- random forest
- fine tuning
- decision trees
- fold cross validation
- feature set
- convolutional neural networks
- cancer classification
- random forests
- decision tree learning algorithms
- ensemble methods
- classification accuracy
- feature space
- ensemble classifier
- text classification
- feature extraction
- machine learning
- object detection
- support vector machine
- majority voting
- benchmark datasets
- feature importance
- classification models
- feature selection
- unsupervised learning
- supervised learning
- feature vectors
- cost sensitive
- machine learning methods
- multi label
- classification algorithm
- machine learning algorithms
- naive bayes
- training samples
- malware detection
- fine tuned
- pairwise