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008			220601s2018 sz | s |||| 0|eng d
020			_a9783031015809
024	7		_a10.1007/978-3-031-01580-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2018 EB
100	1		_aVorobeychik, Yevgeniy _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687857
245	1	0	_aAdversarial Machine Learning _cby Yevgeniy Vorobeychik, Murat Kantarcioglu
250			_a1st edition 2018
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XVII, 152 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Artificial Intelligence and Machine Learning _x1939-4616
505	0		_aList of Figures -- Preface -- Acknowledgments -- Introduction -- Machine Learning Preliminaries -- Categories of Attacks on Machine Learning -- Attacks at Decision Time -- Defending Against Decision-Time Attacks -- Data Poisoning Attacks -- Defending Against Data Poisoning -- Attacking and Defending Deep Learning -- The Road Ahead -- Bibliography -- Authors' Biographies -- Index .
520			_aThe increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop. The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research. Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.
988			_aSynthesis Collection of Technology_2018
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9686845 _aTeoría de juegos
700	1		_aKantarcioglu, Murat _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687858
776	0	8	_iPrinted edition: _z9783031000256
776	0	8	_iPrinted edition: _z9783031004520
776	0	8	_iPrinted edition: _z9783031027086
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01580-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI