An approach of malicious executables detection on black & gray based on adaboost algorithm

Behavioral analysis refers to the technique of deciding whether an application is malicious or not, according to what it does. With behavioral analysis research on executables evolving, it is difficult to classify malicious applications and some legal applications called dasiagray applicationpsila, which are classified as malicious sample by dasiaweakpsila learners. In theory, boosting can be used to significantly reduce the error of dasiaweakpsila learning algorithm that consistently generates classifiers which need only be a little bit better than random guessing. This paper presents an approach based on a new boosting algorithm called AdaBoost, which improves the performance of any dasiaweakpsila learning algorithm. Experiment results show that the method has good classification accuracy in experiment data sets.