Unknown malcode detection using classifiers with optimal training sets

摘要

The present invention is directed to a method for detecting unknown malicious code, such as a virus, a worm, a Trojan Horse or any combination thereof. Accordingly, a Data Set is created, which is a collection of files that includes a first subset with malicious code and a second subset with benign code files and malicious and benign files are identified by an antivirus program. All files are parsed using n-gram moving windows of several lengths and the TF representation is computed for each n-gram in each file. An initial set of top features (e.g., up to 5500) of all n-grams IS selected, based on the DF measure and the number of the top features is reduced to comply with the computation resources required for classifier training, by using features selection methods. The optimal number of features is then determined based on the evaluation of the detection accuracy of several sets of reduced top features and different data sets with different distributions of benign and malicious files are prepared, based on the optimal number, which will be used as training and test sets. For each classifier, the detection accuracy is iteratively evaluated for all combinations of training and test sets distributions, while in each iteration, training a classifier using a specific distribution and testing the trained classifier on all distributions. The optimal distribution that results with the highest detection accuracy is selected for that classifier.