Machine learning algorithms adversarial robustness in signal processing / [electronic resource] :

• 作者： Li, Fuwei.
• 其他作者：
  • Lai, Lifeng.
  • Cui, Shuguang.
  • SpringerLink (Online service)
• 其他題名：
  • Wireless networks.
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Wireless networks,
• 主題： Machine learning. , Computer algorithms.
• ISBN： 9783031163753 (electronic bk.) 、 9783031163746 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Chapter. 1. Introduction -- Chapter. 2. Optimal Feature Manipulation Attacks Against Linear Regression -- Chapter. 3. On the Adversarial Robustness of LASSO Based Feature Selection -- Chapter. 4. On the Adversarial Robustness of Subspace Learning -- Chapter. 5. Summary and Extensions -- Chapter. 6. Appendix.
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• 摘要註: This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing algorithms against adversarial attacks. Since data quality is crucial in signal processing, the adversary that can poison the data will be a significant threat to signal processing. Therefore, it is necessary and urgent to investigate the behavior of machine learning algorithms in signal processing under adversarial attacks. The authors in this book mainly examine the adversarial robustness of three commonly used machine learning algorithms in signal processing respectively: linear regression, LASSO-based feature selection, and principal component analysis (PCA) As to linear regression, the authors derive the optimal poisoning data sample and the optimal feature modifications, and also demonstrate the effectiveness of the attack against a wireless distributed learning system. The authors further extend the linear regression to LASSO-based feature selection and study the best strategy to mislead the learning system to select the wrong features. The authors find the optimal attack strategy by solving a bi-level optimization problem and also illustrate how this attack influences array signal processing and weather data analysis. In the end, the authors consider the adversarial robustness of the subspace learning problem. The authors examine the optimal modification strategy under the energy constraints to delude the PCA-based subspace learning algorithm. This book targets researchers working in machine learning, electronic information, and information theory as well as advanced-level students studying these subjects. R&D engineers who are working in machine learning, adversarial machine learning, robust machine learning, and technical consultants working on the security and
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