Multiple Classifier Systems for More Accurate Java']JavaScript Malware Detection

The researches of JavaScript malware detection focus on machine learning techniques in recent years. These works extract features from JavaScript's abstract syntax tree for the training of classifiers and achieve satisfactory detection results. However, in the training set there exist some scripts that are not so representative and may cause occasional incorrect classification. We propose multiple classifier system (MCS) to reduce this kind of misclassification. As shown in the experiments, the accuracy increases because of the MCS while training time is slightly greater than the original classifier.