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008			230418s2018 sz | s |||| 0|eng d
020			_a9783031023545
024	7		_a10.1007/978-3-031-02354-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.A25 _b2018 EB
100	1		_aYao, Danfeng _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688096 _c(Computer scientist)
245	1	0	_aAnomaly Detection as a Service : _bChallenges, Advances, and Opportunities _cby Danfeng (Daphne) Yao, Xiaokui Shu, Long Cheng, Salvatore J. Stolfo
250			_a1st edition 2018
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XV, 157 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Information Security Privacy and Trust _x1945-9750
505	0		_aPreface -- Acknowledgments -- Introduction -- Threat Models -- Local vs. Global Program Anomaly Detection -- Program Analysis in Data-driven Anomaly Detection -- Anomaly Detection in Cyber-Physical Systems -- Anomaly Detection on Network Traffic -- Automation and Evaluation for Anomaly Detection Deployment -- Anomaly Detection from the Industry's Perspective -- Exciting New Problems and Opportunities -- Bibliography -- Authors' Biographies -- Index.
520			_aAnomaly detection has been a long-standing security approach with versatile applications, ranging from securing server programs in critical environments, to detecting insider threats in enterprises, to anti-abuse detection for online social networks. Despite the seemingly diverse application domains, anomaly detection solutions share similar technical challenges, such as how to accurately recognize various normal patterns, how to reduce false alarms, how to adapt to concept drifts, and how to minimize performance impact. They also share similar detection approaches and evaluation methods, such as feature extraction, dimension reduction, and experimental evaluation. The main purpose of this book is to help advance the real-world adoption and deployment anomaly detection technologies, by systematizing the body of existing knowledge on anomaly detection. This book is focused on data-driven anomaly detection for software, systems, and networks against advanced exploits and attacks, but also touches on a number of applications, including fraud detection and insider threats. We explain the key technical components in anomaly detection workflows, give in-depth description of the state-of-the-art data-driven anomaly-based security solutions, and more importantly, point out promising new research directions. This book emphasizes on the need and challenges for deploying service-oriented anomaly detection in practice, where clients can outsource the detection to dedicated security providers and enjoy the protection without tending to the intricate details.
988			_aSynthesis Collection of Technology_2018
650		7	_2embne _9158200 _aSeguridad informática
700	1		_aShu, Xiaokui _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aCheng, Long _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aStolfo, Salvatore J _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783031002373
776	0	8	_iPrinted edition: _z9783031012266
776	0	8	_iPrinted edition: _z9783031034824
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02354-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE