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A Deep Learning Approach to Image-Based Malware Analysis

  • Gurumayum Akash SharmaEmail author
  • Khundrakpam Johnson Singh
  • Maisnam Debabrata Singh
Conference paper
  • 6 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1119)

Abstract

Malicious software also referred to as “Malware” is one of the serious threats on the Internet today since it has been growing exponentially over the last decade according to research, causing substantial financial trouble to various organizations. Different security companies have been proposing different techniques to defend from this threat which is a major challenge on the complexity and growing volumes. Recently, malware communities and researchers have begun to apply machine learning and deep learning model to detect potential threats. We propose a malware classification model that takes advantage of the potential of deep learning (DL) models using the convolutional neural network (CNN) and combination of machine learning classifier with CNN such as support vector machine (SVM) for classifying their families. Detection of newly released malware using such models would be possible through mathematical function. That is, \( f{:}n \to z \), where n is the given malware and z is their corresponding malware family. Malimg dataset is used to perform the experiment which contains malware image of 25 malware families and 9339 malware samples. CNN has outperformed the CNN-SVM with a test accuracy of 97.5%.

Keywords

Artificial intelligence Deep learning Machine learning Malware classification Support vector machine Convolutional neural networks 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Gurumayum Akash Sharma
    • 1
    Email author
  • Khundrakpam Johnson Singh
    • 1
  • Maisnam Debabrata Singh
    • 1
  1. 1.Department of Computer Science and EngineeringNIT ManipurImphalIndia

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