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710	2		_aSpringerLink (Online service) _9106996
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005			20230102113851.0
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008			191219s2020 gw a o |||| 0|eng d
020			_a9783030336646
024	7		_a10.1007/978-3-030-33664-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5105.543 _b2020 EB
100	1		_aJin, Shi _9672777
245	1	0	_aAnomaly-Detection and Health-Analysis Techniques for Core Router Systems _cby Shi Jin, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu
250			_aPrimera edición 2020
264		1	_aCham _bSpringer _c2020
300			_a1 recurso en línea (XIII, 148 páginas) _b 101 ilustraciones, 90 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
505	0		_aIntroduction -- Anomaly Detection Using Correlation-Based Time-Series Analysis -- Changepoint-based Anomaly Detection -- Hierarchical Symbol-based Health-Status Analysis -- Self-Learning Health-Status Analysis -- Conclusion.
520	3		_aThis book tackles important problems of anomaly detection and health status analysis in complex core router systems, integral to today's Internet Protocol (IP) networks. The techniques described provide the first comprehensive set of data-driven resiliency solutions for core router systems. The authors present an anomaly detector for core router systems using correlation-based time series analysis, which monitors a set of features of a complex core router system. They also describe the design of a changepoint-based anomaly detector such that anomaly detection can be adaptive to changes in the statistical features of data streams. The presentation also includes a symbol-based health status analyzer that first encodes, as a symbol sequence, the long-term complex time series collected from a number of core routers, and then utilizes the symbol sequence for health analysis. Finally, the authors describe an iterative, self-learning procedure for assessing the health status. Enables Accurate Anomaly Detection Using Correlation-Based Time-Series Analysis; Presents the design of a changepoint-based anomaly detector; Includes Hierarchical Symbol-based Health-Status Analysis; Describes an iterative, self-learning procedure for assessing the health status.
988			_aPrimersemestre_2020_Engineering
650		7	_2embne _9158896 _aEncaminadores (Redes Informáticas)
700	1		_aZhang, Zhaobo _eautor
700	1		_aChakrabarty, Krishnendu _eautor
700	1		_aGu, Xinli _eautor
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783030336639
776	0	8	_iPrinted edition: _z9783030336653
776	0	8	_iPrinted edition: _z9783030336660
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-33664-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2020 _dz _eu _zSI