Securing the Deep Fraud Detector in Large-Scale E-Commerce Platform via Adversarial Machine Learning Approach-Reference-Cited by-同舟云学术

Securing the Deep Fraud Detector in Large-Scale E-Commerce Platform via Adversarial Machine Learning Approach

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:The World Wide Web Conference on - WWW '19
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Author:

Guo Qingyu¹,Li Zhao²,An Bo¹,Hui Pengrui²,Huang Jiaming²,Zhang Long²,Zhao Mengchen¹

Affiliation:

1. Nanyang Technological University, Singapore

2. Alibaba Group, China

Publisher

ACM Press

Reference36 articles.

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2. Jacob Buckman, Aurko Roy, Colin Raffel, and Ian Goodfellow. 2018. Thermometer Encoding: One Hot Way to Resist Adversarial Examples. (2018).

3. Nicholas Carlini, Guy Katz, Clark Barrett, and David L Dill. 2017. Provably Minimally-Distorted Adversarial Examples. arXiv1709(2017).

4. Nicholas Carlini and David A. Wagner. 2017. Towards Evaluating the Robustness of Neural Networks. In 2017 IEEE Symposium on Security and Privacy (SP'17). 39-57.

5. Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh. 2018. EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI'18). 10-17.

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