SBMDS: an interpretable string based malware detection system using SVM ensemble with bagging.
Yanfang YeLifei ChenDingding WangTao LiQingshan JiangMin ZhaoPublished in: J. Comput. Virol. (2009)
Keyphrases
- malware detection
- imbalanced data
- ensemble methods
- ensemble classifier
- ensemble learning
- random subspace
- generalization ability
- random forest
- base classifiers
- majority voting
- malicious executables
- ensemble classification
- support vector machine svm
- random forests
- training set
- ensemble members
- ensemble selection
- multi class
- support vector machine
- learning machines
- support vector
- anomaly detection
- classifier ensemble
- feature selection
- subspace methods
- base learners
- decision trees
- prediction accuracy
- decision stumps
- application programming interface
- svm classifier
- knn
- machine learning
- training data
- pattern matching
- classification algorithm
- class label noise
- decision tree ensembles
- randomized trees
- regular expressions
- machine learning methods
- decision forest
- benchmark datasets
- feature ranking
- concept drift
- tree ensembles
- feature set
- individual classifiers
- weak classifiers
- cost sensitive
- feature subset