| 書目名稱 | Machine Learning Algorithms | | 副標(biāo)題 | Adversarial Robustne | | 編輯 | Fuwei Li,Lifeng Lai,Shuguang Cui | | 視頻video | http://file.papertrans.cn/621/620378/620378.mp4 | | 概述 | Demonstrates how machine learning is widely used in signal processing.Investigates the adversarial robustness of signal processing algorithms.Conducts an attack on a principal regression problem | | 叢書名稱 | Wireless Networks | | 圖書封面 |  | | 描述 | .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 | | 出版日期 | Book 2022 | | 關(guān)鍵詞 | Machine learning; adversarial machine learning; security-critical machine learning; interpretable machi | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-16375-3 | | isbn_softcover | 978-3-031-16377-7 | | isbn_ebook | 978-3-031-16375-3Series ISSN 2366-1186 Series E-ISSN 2366-1445 | | issn_series | 2366-1186 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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