Speedy and efficient malwares images classifier using reduced GIST features for a new defense guide

Abstract

Malwares attacks are becoming increasingly destructive. Hackers target all types of devices from big to the most little ones. Researcher's communities in cybersecurity field are working hard to defend malwares attacks as well as any other malicious activity. In fact, the primary goal is to defend cyberattacks as fast as possible to avoid catastrophic damages. In this paper, we proposed new cybersecurity architecture specialized in malwares attacks defense. This proposal puts together four layers based on malwares behaviors. In addition, we perform malware classifier using malware visualization technique, GIST descriptor features and K-Nearest Neighbor algorithm. The classifier is able to put each input malware image into its corresponding family. Families distribution is been divided by malwares behaviors. For the purpose of attaining speedy malwares classifier, we use Univariate Feature Selection technique to reduce GIST feature. So we succeeded in getting from 320 to only 50 features in less timing with very close accuracy of 97,67%.