Powered by OpenAIRE graph
Found an issue? Give us feedback
addClaim

Malware detection using malware image and deep learning

Authors: Sunoh Choi; Sungwook Jang; Youngsoo Kim; Jong-Hyun Kim;

Malware detection using malware image and deep learning

Abstract

These days a lot of malware are generated. In order to deal with the new malware, we need new ways to detect malware. In this paper, we introduce a method to detect malware using deep learning. First, we generate images from benign files and malware. Second, by using deep learning, we train a model to detect malware. Then, by the trained model, we detect malware. By using malware images and deep learning, we can detect malware fast since we do not need any static analysis or dynamic analysis.

Related Organizations
  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    24
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
24
Top 10%
Top 10%
Top 10%
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!