Integrated Framework for Classification of Malwares

Abstract

Malware is one of the most terrible and major security threats facing the Internet today. It is evolving, becoming more sophisticated and using new ways to target computers and mobile devices. The traditional defences like antivirus softwares typically rely on signature based methods and are unable to detect previously unseen malwares. Machine learning approaches have been adopted to classify malwares based on the features extracted using static or dynamic analysis. Both type of malware analysis have their pros and cons. In this paper, we propose a classification framework which uses integration of both static and dynamic features for distinguishing malwares from clean files. A real world corpus of recent malwares is used to validate the proposed approach. The experimental results, based on a dataset of 998 malwares and 428 cleanware files provide an accuracy of 99.58% indicating that the hybrid approach enhances the accuracy rate of malware detection and classification over the results obtained when these features are considered separately.