From Blue-Sky to Practical Adversarial Learning

Aminollah Khormali, Ahmed Abusnaina, Songqing Chen, Dae Hun Nyang, David Mohaisen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

The state-of-the-art of adversarial machine learning on malware detection systems generally yield unexecutable samples. In this work, we make the case for understanding the robustness of visualization-based malware detection system against adversarial examples (AEs) that not only are able to fool models, but also maintain the executability of the original input. To motivate for our vision, we first investigate the application of existing off-the-shelf adversarial attack approaches on malware detection systems through which we found that those approaches do not necessarily maintain the functionality of the original inputs. Then, we discuss an approach for achieving a high misclassification rate and maintaining the executability and functionality of the original input. We use visualization-based malware detection as an example to highlight the gap between blue-sky research that focuses on aspect of the learning process, and call for more practical techniques that respect the semantics of the underlying applications.

Original languageEnglish
Title of host publicationProceedings - 2020 2nd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages118-127
Number of pages10
ISBN (Electronic)9781728185439
DOIs
StatePublished - Oct 2020
Event2nd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2020 - Virtual, Atlanta, United States
Duration: 1 Dec 20203 Dec 2020

Publication series

NameProceedings - 2020 2nd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2020

Conference

Conference2nd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2020
Country/TerritoryUnited States
CityVirtual, Atlanta
Period1/12/203/12/20

Bibliographical note

Funding Information:
This work was supported in part by CyberFlorida Collaborative Seed Award (2020), NSF CNS-2007153, and the National Research Foundation of South Korea under grant NRF-2016K1A1A2912757.

Funding Information:
ACKNOWLEDGEMENT This work was supported in part by CyberFlorida Collaborative Seed Award (2020), NSF CNS-2007153, and the National Research Foundation of South Korea under grant NRF-2016K1A1A2912757.

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Adversarial Examples
  • Deep Learning
  • Malware Detection
  • Visualization

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