@inproceedings{a8d8a91b9973439da78bd58c8d51591d,
title = "From Blue-Sky to Practical Adversarial Learning",
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.",
keywords = "Adversarial Examples, Deep Learning, Malware Detection, Visualization",
author = "Aminollah Khormali and Ahmed Abusnaina and Songqing Chen and Nyang, {Dae Hun} and David Mohaisen",
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: {\textcopyright} 2020 IEEE.; null ; Conference date: 01-12-2020 Through 03-12-2020",
year = "2020",
month = oct,
doi = "10.1109/TPS-ISA50397.2020.00025",
language = "English",
series = "Proceedings - 2020 2nd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "118--127",
booktitle = "Proceedings - 2020 2nd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2020",
}