Robust Machine Learning in Adversarial Setting with Provable Guarantee

  • Author : Yizhen Wang
  • Publsiher : Anonim
  • Release : 16 April 2021
  • ISBN : OCLC:1149141432
  • Page : 178 pages
  • Rating : 4/5 from 21 voters

Download or read online book entitled Robust Machine Learning in Adversarial Setting with Provable Guarantee written by Yizhen Wang and published by Anonim. This book was released on 16 April 2021 with total page 178 pages. Available in PDF, EPUB and Kindle. Get best books that you want by click Get Book Button and Read as many books as you like. Book Excerpt : Over the last decade, machine learning systems have achieved state-of-the-art performance in many fields, and are now used in increasing number of applications. However, recent research work has revealed multiple attacks to machine learning systems that significantly reduce the performance by manipulating the training or test data. As machine learning is increasingly involved in high-stake decision making processes, the robustness of machine learning systems in adversarial environment becomes a major concern. This dissertation attempts to build machine learning systems robust to such adversarial manipulation with the emphasis on providing theoretical performance guarantees. We consider adversaries in both test and training time, and make the following contributions. First, we study the robustness of machine learning algorithms and model to test-time adversarial examples. We analyze the distributional and finite sample robustness of nearest neighbor classification, and propose a modified 1-Nearest-Neighbor classifier that both has theoretical guarantee and empirical improvement in robustness. Second, we examine the robustness of malware detectors to program transformation. We propose novel attacks that evade existing detectors using program transformation, and then show program normalization as a provably robust defense against such transformation. Finally, we investigate data poisoning attacks and defenses for online learning, in which models update and predict over data stream in real-time. We show efficient attacks for general adversarial objectives, analyze the conditions for which filtering based defenses are effective, and provide practical guidance on choosing defense mechanisms and parameters.

Robust Machine Learning in Adversarial Setting with Provable Guarantee

Robust Machine Learning in Adversarial Setting with Provable Guarantee
Author: Yizhen Wang
Publisher: Unknown
Relase: 2020
ISBN: OCLC:1149141432
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