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008			220715s2022 sz | s |||| 0|eng d
020			_a9783030970871
024	7		_a10.1007/978-3-030-97087-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
245	1	0	_aArtificial Intelligence for Cybersecurity _cedited by Mark Stamp, Corrado Aaron Visaggio, Francesco Mercaldo, Fabio Di Troia
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XVI, 380 páginas) _b184 ilustraciones, 155 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aAdvances in Information Security _x2512-2193 _v54
505	0		_aPart I: Malware-Related Topics -- Generation of Adversarial Malware and Benign Examples using Reinforcement Learning -- Auxiliary-Classifier GAN for Malware Analysis -- Assessing the Robustness of an Image-based Malware Classifier with Small Level Perturbations Techniques -- Detecting Botnets Through Deep Learning and Network Flow Analysis -- Interpretability of Machine Learning-Based Results of Malware Detection Using a Set of Rules -- Mobile Malware Detection using Consortium Blockchain -- BERT for Malware Classification -- Machine Learning for Malware Evolution Detection -- Part II: Other Security Topics -- Gambling for Success: The Lottery Ticket Hypothesis in Deep Learning-based Side-channel Analysis -- Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication -- Clickbait Detection for YouTube Videos -- Survivability Using Artificial Intelligence Assisted Cyber Risk Warning -- Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics -- Machine Learning-Based Analysis of Free-Text Keystroke Dynamic -- Free-Text Keystroke Dynamics for User Authentication.
520			_aThis book explores new and novel applications of machine learning, deep learning, and artificial intelligence that are related to major challenges in the field of cybersecurity. The provided research goes beyond simply applying AI techniques to datasets and instead delves into deeper issues that arise at the interface between deep learning and cybersecurity. This book also provides insight into the difficult "how" and "why" questions that arise in AI within the security domain. For example, this book includes chapters covering "explainable AI", "adversarial learning", "resilient AI", and a wide variety of related topics. It's not limited to any specific cybersecurity subtopics and the chapters touch upon a wide range of cybersecurity domains, ranging from malware to biometrics and more. Researchers and advanced level students working and studying in the fields of cybersecurity (equivalently, information security) or artificial intelligence (including deep learning, machine learning, big data, and related fields) will want to purchase this book as a reference. Practitioners working within these fields will also be interested in purchasing this book.
700	1		_aStamp, Mark _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aAaron Visaggio, Corrado _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMercaldo, Francesco _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aDi Troia, Fabio _eeditor literario _0(orcid)0000-0003-2355-7146 _1https://orcid.org/0000-0003-2355-7146 _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030970864
776	0	8	_iPrinted edition: _z9783030970888
776	0	8	_iPrinted edition: _z9783030970895
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-97087-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_Computer_2022
999			_c394736 _d394736