Combinatorial Boosting of Classifiers for Moving Target Defense Against Adversarial Evasion Attacks.
Rauf IzmailovPeter LinSridhar VenkatesanShridatt SugrimPublished in: MTD@CCS (2021)
Keyphrases
- moving target defense
- countermeasures
- ensemble learning
- weak classifiers
- randomized trees
- ensemble classifier
- boosting framework
- weak learners
- boosting algorithms
- feature selection
- majority voting
- improving classification accuracy
- strong classifier
- adaboost algorithm
- decision stumps
- support vector
- accurate classifiers
- decision trees
- multiple classifier systems
- ensemble methods
- ensemble classification
- training data
- linear classifiers
- training set
- information security
- watermarking scheme
- training samples
- bayesian classifiers
- loss function
- boosted classifiers
- multiclass classification
- base classifiers
- learning algorithm
- support vector machine
- multi agent
- face detection
- weighted voting
- test set
- classification trees
- random forests
- machine learning algorithms
- discriminative classifiers
- ddos attacks
- classifier combination
- privacy preserving