Cyber Security Meets Machine Learning (Paperback, 1st ed. 2021)

Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users. This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning. Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Release date:	July 2022
First published:	2021
Editors:	Xiaofeng Chen • Willy Susilo • Elisa Bertino
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	163
Edition:	1st ed. 2021
ISBN-13:	978-981-3367-28-9
Categories:	Books > Computing & IT > Computer communications & networking > Network security Books > Computing & IT > Internet > Network computers Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision Promotions
LSN:	981-3367-28-8
Barcode:	9789813367289

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