000			03154nam a2200433 i 4500
710	2		_aSpringerLink (Online service) _9106996
999			_c119286 _d119286
001			119286
003			ES-MaUEC
005			20230102113939.0
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007			cr nn nnnaamaa
008			200208s2020 gw | s |||| 0|eng d
020			_a9783030379629
024	7		_a10.1007/978-3-030-37962-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTJ213 _b2020 EB
100	1		_aRokka Chhetri, Sujit _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9673440
245	1	0	_aData-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis _cby Sujit Rokka Chhetri, Mohammad Abdullah Al Faruque.
250			_aFirst edition 2020.
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XVI, 235 páginas) _b111 ilustraciones, 106 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aArchivo de texto _bPDF
490	0		_aEngineering (Springer-11647)
520	3		_aThis book provides a new perspective on modeling cyber-physical systems (CPS), using a data-driven approach. The authors cover the use of state-of-the-art machine learning and artificial intelligence algorithms for modeling various aspect of the CPS. This book provides insight on how a data-driven modeling approach can be utilized to take advantage of the relation between the cyber and the physical domain of the CPS to aid the first-principle approach in capturing the stochastic phenomena affecting the CPS. The authors provide practical use cases of the data-driven modeling approach for securing the CPS, presenting novel attack models, building and maintaining the digital twin of the physical system. The book also presents novel, data-driven algorithms to handle non- Euclidean data. In summary, this book presents a novel perspective for modeling the CPS. · Provides an introduction to the data-driven modeling of cyber-physical systems (CPS), to aid in capturing the stochastic phenomenon affecting CPS; · Describes practical applications for securing the CPS as well as building the digital twin of the physical twin of CPS; · Includes coverage of machine learning and artificial intelligence algorithms for data-driven modeling of the CPS; Provides novel algorithms for handling not just Euclidean data, but also non-Euclidean data.
988			_aSpringer_Engineering_31032020
650		7	_2embne _aControl automático _9405125
650		7	_2embne _9138450 _aCibernética
700	1		_aAl Faruque, Mohammad Abdullah _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9673441
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783030379612
776	0	8	_iPrinted edition: _z9783030379636
776	0	8	_iPrinted edition: _z9783030379643
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-37962-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2020 _dz _ek _zSI