Robustness Analysis of CNN-based Malware Family Classification Methods Against Various Adversarial Attacks

Seok Hwan Choi, Jin Myeong Shin, Peng Liu, Yoon Ho Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

As malware family classification methods, image-based classification methods have attracted much attention. Especially, due to the fast classification speed and the high classification accuracy, Convolutional Neural Network (CNN)-based malware family classification methods have been studied. However, previous studies on CNN-based classification methods focused only on improving the classification accuracy of malware families. That is, previous studies did not consider the cases that the accuracy of CNN-based malware classification methods can be decreased under the existence of adversarial attacks. In this paper, we analyze the robustness of various CNN-based malware family classification models under adversarial attacks. While adding imperceptible non-random perturbations to the input image, we measured how the accuracy of the CNN-based malware family classification model can be affected. Also, we showed the influence of three significant visualization parameters(i.e., the size of input image, dimension of input image, and conversion color of a special character)on the accuracy variation under adversarial attacks. From the evaluation results using the Microsoft malware dataset, we showed that even the accuracy over 98% of the CNN-based malware family classification method can be decreased to less than 7%.

Original languageEnglish (US)
Title of host publication2019 IEEE Conference on Communications and Network Security, CNS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538671177
DOIs
StatePublished - Jun 2019
Event2019 IEEE Conference on Communications and Network Security, CNS 2019 - Washington, United States
Duration: Jun 10 2019Jun 12 2019

Publication series

Name2019 IEEE Conference on Communications and Network Security, CNS 2019

Conference

Conference2019 IEEE Conference on Communications and Network Security, CNS 2019
Country/TerritoryUnited States
CityWashington
Period6/10/196/12/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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