Overview
- 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
Part of the book series: Wireless Networks (WN)
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About this book
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 robustness of machine learning are likely to purchase this book as a reference guide.
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Keywords
- Machine learning
- adversarial machine learning
- security-critical machine learning
- interpretable machine learning
- adversarial robustness
- adversarial attack
- linear regression
- LASSO
- subspace learning
- principal component analysis
- non-convex optimization
- bi-level optimization
- alternating optimization
- signal processing
Table of contents (5 chapters)
Authors and Affiliations
About the authors
Shuguang Cui received his Ph.D in Electrical Engineering from Stanford University, California, USA, in 2005. Afterwards, he has been working as assistant, associate, full, Chair Professor in Electrical and Computer Engineering at the Univ. of Arizona, Texas A&M University, UC Davis, and CUHK at Shenzhen respectively. He has also served as the Executive Dean for the School of Science and Engineering at CUHK, Shenzhen, the Director for the Future Network of Intelligence Institute, and the Executive Vice Director at Shenzhen Research Institute of Big Data. His current research interests focus on data driven large-scale system control and resource management, large data set analysis, IoT system design, energy harvesting based communication system design, and cognitive network optimization. He was selected as the Thomson Reuters Highly Cited Researcher and listed in the Worlds’ Most Influential Scientific Minds by ScienceWatch in 2014. He was the recipient of the IEEE Signal Processing Society 2012 Best Paper Award. He has served as the general co-chair and TPC co-chairs for many IEEE conferences. He has also been serving as the area editor for IEEE Signal Processing Magazine, and associate editors for IEEE Transactions on Big Data, IEEE Transactions on Signal Processing, IEEE JSAC Series on Green Communications and Networking, and IEEE Transactions on Wireless Communications. He has been the elected member for IEEE Signal Processing Society SPCOM Technical Committee (2009~2014) and the elected Chair for IEEE ComSoc Wireless Technical Committee (2017~2018). He is a member of the SteeringCommittee for IEEE Transactions on Big Data and the Chair of the Steering Committee for IEEE Transactions on Cognitive Communications and Networking. He was also a member of the IEEE ComSoc Emerging Technology Committee. He was elected as an IEEE Fellow in 2013, an IEEE ComSoc Distinguished Lecturer in 2014, and IEEE VT Society Distinguished Lecturer in 2019. He has won the IEEE ICC best paper award, ICIP best paper finalist, and the IEEE Globecom best paper award all in 2020.
Bibliographic Information
Book Title: Machine Learning Algorithms
Book Subtitle: Adversarial Robustness in Signal Processing
Authors: Fuwei Li, Lifeng Lai, Shuguang Cui
Series Title: Wireless Networks
DOI: https://doi.org/10.1007/978-3-031-16375-3
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-16374-6Published: 16 November 2022
Softcover ISBN: 978-3-031-16377-7Published: 17 November 2023
eBook ISBN: 978-3-031-16375-3Published: 14 November 2022
Series ISSN: 2366-1186
Series E-ISSN: 2366-1445
Edition Number: 1
Number of Pages: IX, 104
Number of Illustrations: 1 b/w illustrations, 22 illustrations in colour
Topics: Machine Learning, Wireless and Mobile Communication, Artificial Intelligence