000			02397nam a2200373 i 4500
001			CR9781107338548
003			UkCbUP
005			20240807165923.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			130215s2019||||enk o ||1 0|eng|d
020			_a9781107338548 (ebook)
020			_z9781107043466 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQ325.5 _b.J69 2019
082	0	0	_a006.3/1 _223
100	1		_aJoseph, Anthony D., _eauthor.
245	1	0	_aAdversarial machine learning / _cAnthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, J. D. Tygar.
264		1	_aCambridge : _bCambridge University Press, _c2019.
300			_a1 online resource (xii, 325 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 13 Mar 2019).
520			_aWritten by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race.
650		0	_aMachine learning.
650		0	_aComputer security.
700	1		_aNelson, Blaine, _eauthor.
700	1		_aRubinstein, Benjamin I. P., _eauthor.
700	1		_aTygar, J. D., _eauthor.
776	0	8	_iPrint version: _z9781107043466
856	4	0	_uhttps://www.cambridge.org/core/product/identifier/9781107338548/type/BOOK
942			_2ddc _cEB
999			_c9943 _d9943