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Integrated Static Analysis for Malware Variants Detection

  • Rinu Rani JoseEmail author
  • A. Salim
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 98)

Abstract

The influence of malware is growing exponentially by the invention of new malicious programs and potentially unwanted applications. Malware detection is critical for protection against data theft, security breaches and other related dangers. But the detection techniques continue to be challenging, as the attackers invent new techniques to resist the detection methods. Thus efficient techniques are required for the identification of malware variants or samples. This paper proposes an integrated static method for the efficient detection of malware. The proposed approach is a combination of two different static models. An image based model which uses image features for the analysis and a code based model which uses opcodes for the analysis of malware. Machine learning techniques are used for the classification of samples. The combined model efficiently classifies the malware variants with an accuracy of 95% and is resistant to the code obfuscation techniques associated with traditional static analysis.

Keywords

Malware detection Static analysis Code visualization Opcode features Machine learning 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of Engineering TrivandrumThiruvananthapuramIndia

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