A Feature Selection for Malicious Detection

Abstract

The detection of unknown malicious executables is beyond the capability of many existing detection approaches. Machine learning or data mining methods can identify new or unknown malicious executables with some degree of success. Feature selection is a key to apply data mining or machine learning to successfully detect malicious executables. We propose a method to extract features which are most representative of viral properties. We show that our classifier, based on strings, achieves high detection rates and can be expected to perform as well in real-world conditions.