Adversarially Robust Malware Detection Using Monotonic Classification-Reference-Cited by-同舟云学术

Adversarially Robust Malware Detection Using Monotonic Classification

Published:2018-03-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Fourth ACM International Workshop on Security and Privacy Analytics
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Author:

Íncer Romeo Íñigo¹,Theodorides Michael¹,Afroz Sadia²,Wagner David¹

Affiliation:

1. University of California, Berkeley, Berkeley, CA, USA

2. University of California, Berkeley & International Computer Science Institute, Berkeley, CA, USA

Funder

Center for Long-Term Cybersecurity

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3180445.3180449

Reference34 articles.

1. Adding Robustness to Support Vector Machines Against Adversarial Reverse Engineering

2. Application of the Back Propagation Neural Network Algorithm with Monotonicity Constraints for Two-Group Classification Problems

3. Adding monotonicity to learning algorithms may impair their accuracy

4. Arjun Nitin Bhagoji Daniel Cullina and Prateek Mittal. 2017. Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers. arXiv preprint arXiv:1704.02654 (2017). Arjun Nitin Bhagoji Daniel Cullina and Prateek Mittal. 2017. Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers. arXiv preprint arXiv:1704.02654 (2017).

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