Ensemble Machine Learning Approach for Android Malware Classification Using Hybrid Features.
Abdurrahman PektasTankut AcarmanPublished in: CORES (2017)
Keyphrases
- machine learning
- classification accuracy
- feature selection
- feature vectors
- feature set
- feature extraction
- feature space
- classification models
- pattern recognition
- machine learning methods
- machine learning approaches
- decision trees
- final classification
- support vector machine
- feature ranking
- increase classification accuracy
- multiple classifiers
- feature construction
- text classification
- ensemble methods
- feature subset
- decision tree classifiers
- classification method
- feature analysis
- wrapper feature selection
- classification process
- svm classifier
- training set
- learning algorithm
- image classification
- supervised learning
- extracting features
- supervised classification
- feature selection algorithms
- individual features
- machine learning algorithms
- benchmark datasets
- extracted features
- single feature
- weak classifiers
- class labels
- supervised machine learning
- support vector
- neural network
- ensemble classifier
- model selection
- training samples
- classifier ensemble
- image features
- active learning
- feature values
- reverse engineering
- malware detection
- training data
- high dimensionality
- random forest
- machine learning models
- classification algorithm