A proposals of convolution neural network system for malicious code analysis based on cloud systems

  • Abstract
  • Keywords
  • References
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  • Abstract

    Background/Objectives: In the information security field, artificial intelligence must be applied first. This is because the frequency of malicious code is too high and the processing method is too difficult, which is very difficult for human to handle.

    Methods/Statistical analysis: In this paper, we developed a program to classify malicious codes into images and a Tensorflow system to classify malicious codes. The malware used as input was the computer virus code used in the BIG 2015 Challenge. This dataset, called a Kaggle dataset, consists of 10,868 bytes of train set.

    Findings: We used the Tensorflow SLIM library to develop this machine learning malware learning machine. This resulted in more than 80% accuracy. Especially, when the CRIS-Ensemble algorithm was added, the accuracy was 97%. The study of malicious code analysis using machine learning consists of two major parts. First, the process of making the virus into images is important. To classify 10,868 Kaggle malware datasets that the BIG 2015 winner showed 99.6% accuracy, Tensorflow's accuracy and parameter tuning are important, but finding the way to make good images is the most important technique

    Improvements/Applications: The results show that the malicious code classification system using machine learning can be an effective method to classify malicious code of malicious code by the accuracy of the result and ease of use.


  • Keywords

    Machine Learning; Tensorflow; Malware Code; Malware Datasets; Convolution Neural Networks

  • References

      [1] Malware Images: Visualization and Automatic Classification, Nataraj, S. Karthikeyan, University of California, Santa Barbara, 2010 ACM 1-58113-000-0/00/0010.

      [2] ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, University of Toronto.

      [3] TensorFlow: A system for large-scale machine learning, Mart´ınAbadi, Paul Barham, 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16), November 2–4, 2016 •Savannah, GA, USA,, https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf.

      [4] Xiaofang, Ban, Chen Li, Hu Weihua, and Wu Qu. "Malware variant detection using similarity search over content fingerprint", The 26th Chinese Control and Decision Conference (2014 CCDC), 2014.




Article ID: 11040
DOI: 10.14419/ijet.v7i2.12.11040

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