AI-Powered GUI Attack and Its Defensive Methods-Reference-Cited by-同舟云学术

AI-Powered GUI Attack and Its Defensive Methods

Published:2020-04-02 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2020 ACM Southeast Conference
language:
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Author:

Yu Ning¹,Tuttle Zachary¹,Thurnau Carl Jake¹,Mireku Emmanuel¹

Affiliation:

1. State University of New York The College at Brockport, Brockport, New York, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3374135.3385270

Reference27 articles.

1. A. K. Boyat and B. K. Joshi. 2015. A Review Paper: Noise Models in Digital Image Processing. CoRR abs/1505.03489 (2015). arXiv:1505.03489 http://arxiv.org/abs/1505.03489 A. K. Boyat and B. K. Joshi. 2015. A Review Paper: Noise Models in Digital Image Processing. CoRR abs/1505.03489 (2015). arXiv:1505.03489 http://arxiv.org/abs/1505.03489

2. N. Carlini and D. A. Wagner. 2016. Defensive Distillation is Not Robust to Adversarial Examples. CoRR abs/1607.04311 (2016). arXiv:1607.04311 http://arxiv.org/abs/1607.04311 N. Carlini and D. A. Wagner. 2016. Defensive Distillation is Not Robust to Adversarial Examples. CoRR abs/1607.04311 (2016). arXiv:1607.04311 http://arxiv.org/abs/1607.04311

3. N. Carlini and D. A. Wagner. 2016. Towards Evaluating the Robustness of Neural Networks. CoRR abs/1608.04644 (2016). arXiv:1608.04644 http://arxiv.org/abs/1608.04644 N. Carlini and D. A. Wagner. 2016. Towards Evaluating the Robustness of Neural Networks. CoRR abs/1608.04644 (2016). arXiv:1608.04644 http://arxiv.org/abs/1608.04644

4. Q. Ding Z. Li S. Haeri and L. Trajković. 2018. Application Of Machine Learning Techniques To Detecting Anomalies In Communication Networks: Datasets And Feature Selection Algorithms. In Cyber Threat Intelligence. Springer 47--70. Q. Ding Z. Li S. Haeri and L. Trajković. 2018. Application Of Machine Learning Techniques To Detecting Anomalies In Communication Networks: Datasets And Feature Selection Algorithms. In Cyber Threat Intelligence. Springer 47--70.

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