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020 _a9783031015809
_9978-3-031-01580-9
024 7 _a10.1007/978-3-031-01580-9
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aVorobeychik, Yevgeniy.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987589
245 1 0 _aAdversarial Machine Learning
_h[electronic resource] /
_cby Yevgeniy Vorobeychik, Murat Kantarcioglu.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXVII, 152 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_987591
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aKantarcioglu, Murat.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987593
710 2 _aSpringerLink (Online service)
_987594
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000256
776 0 8 _iPrinted edition:
_z9783031004520
776 0 8 _iPrinted edition:
_z9783031027086
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_987595
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01580-9
912 _aZDB-2-SXSC
942 _cEBK
999 _c86121
_d86121