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003			ES-MaUEC
005			20230102114746.0
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008			210122s2021 gw a o |||| 0|eng d
020			_a9783030556921
024	7		_a10.1007/978-3-030-55692-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.A25 _b2021 EB
245	1	0	_aAdversary-aware learning techniques and trends in cybersecurity _cedited by Prithviraj Dasgupta, Joseph B Collins, Ranjeev Mittu
250			_aFirst edition 2021
264		1	_aCham, Switzerland _bSpringer International Publising _c2021
300			_a1 recurso en línea (X, 227 páginas) _b68 ilustraciones, 50 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
505	0		_aPart I: Game-Playing AI and Game Theory-based Techniques for Cyber Defenses -- 1. Rethinking Intelligent Behavior as Competitive Games for Handling Adversarial Challenges to Machine Learning -- 2. Security of Distributed Machine Learning:A Game-Theoretic Approach to Design Secure DSVM -- 3. Be Careful When Learning Against Adversaries: Imitative Attacker Deception in Stackelberg Security Games -- Part II: Data Modalities and Distributed Architectures for Countering Adversarial Cyber Attacks -- 4. Adversarial Machine Learning in Text: A Case Study of Phishing Email Detection with RCNN model -- 5. Overview of GANs for Image Synthesis and Detection Methods -- 6. Robust Machine Learning using Diversity and Blockchain -- Part III: Human Machine Interactions and Roles in Automated Cyber Defenses -- 7. Automating the Investigation of Sophisticated Cyber Threats with Cognitive Agents -- 8. Integrating Human Reasoning and Machine Learning to Classify Cyber Attacks -- 9. Homology as an Adversarial Attack Indicator -- Cyber-(in)security, revisited: Proactive Cyber-defenses, Interdependence and Autonomous Human Machine Teams (A-HMTs).
520	3		_aThis book is intended to give researchers and practitioners in the cross-cutting fields of artificial intelligence, machine learning (AI/ML) and cyber security up-to-date and in-depth knowledge of recent techniques for improving the vulnerabilities of AI/ML systems against attacks from malicious adversaries. The ten chapters in this book, written by eminent researchers in AI/ML and cyber-security, span diverse, yet inter-related topics including game playing AI and game theory as defenses against attacks on AI/ML systems, methods for effectively addressing vulnerabilities of AI/ML operating in large, distributed environments like Internet of Things (IoT) with diverse data modalities, and, techniques to enable AI/ML systems to intelligently interact with humans that could be malicious adversaries and/or benign teammates. Readers of this book will be equipped with definitive information on recent developments suitable for countering adversarial threats in AI/ML systems towards making them operate in a safe, reliable and seamless manner.
988			_aSpringer_Computer_2021
650		7	_2embne _aSeguridad informática _9158200
650		7	_2embne _aAgentes inteligentes (Programas de ordenador) _9160930
700	1		_aDasgupta, Prithviraj _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aCollins, Joseph B _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMittu, Ranjeev _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-55692-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2021 _dz _eb _zSI