Towards robust CNN-based malware classifiers using adversarial examples generated based on two saliency similarities
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-08590-1.pdf
Reference64 articles.
1. Al-Dujaili A, Huang A, Hemberg E, et al (2018) Adversarial deep learning for robust detection of binary encoded malware. In: 2018 IEEE Security and Privacy Workshops (SPW), IEEE, pp 76–82
2. Al-Dujaili A, Srikant S, Hemberg E, et al (2019) On the application of Danskin’s theorem to derivative-free minimax problems. In: AIP conference proceedings, AIP Publishing LLC, p 020026
3. Anderson B, McGrew D (2017) Machine learning for encrypted malware traffic classification: accounting for noisy labels and non-stationarity. In: Proceedings of the 23rd ACM SIGKDD, pp 1723–1732
4. Andriushchenko M, Flammarion N (2020) Understanding and improving fast adversarial training. Adv Neural Inf Process Syst 33:16048–16059
5. Bakour K, Ünver HM (2021) Deepvisdroid: android malware detection by hybridizing image-based features with deep learning techniques. Neural Comput Appl 33(18):11,499-11,516
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