000			03551nam a22005175i 4500
001			978-0-387-27636-6
003			DE-He213
005			20250710083939.0
007			cr nn 008mamaa
008			100301s2006 xxu| s |||| 0|eng d
020			_a9780387276366 _a99780387276366
024	7		_a10.1007/0-387-27636-X _2doi
082	0	4	_a005.82 _223
100	1		_aAxelsson, Stefan. _eauthor.
245	1	0	_aUnderstanding Intrusion Detection Through Visualization _h[recurso electrónico] / _cby Stefan Axelsson, David Sands.
264		1	_aBoston, MA : _bSpringer US, _c2006.
300			_aXX, 145 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdvances in Information Security, _x1568-2633 ; _v24
505	0		_aAn Introduction to Intrusion Detection -- The Base-Rate Fallacy and the Difficulty of Intrusion Detection -- Visualizing Intrusions: Watching the Webserver -- Combining a Bayesian Classifier with Visualization: Understanding the IDS -- Visualizing the Inner Workings of a Self Learning Classifier: Improving the Usability of Intrusion Detection Systems -- Visualization for Intrusion Detection-Hooking the Worm -- Epilogue.
520			_aWith the ever increasing use of computers for critical systems, computer security that protects data and computer systems from intentional, malicious intervention, continues to attract significant attention. Among the methods for defense, the application of a tool to help the operator identify ongoing or already perpetrated attacks (intrusion detection), has been the subject of considerable research in the past ten years. A key problem with current intrusion detection systems is the high number of false alarms they produce. Understanding Intrusion Detection through Visualization presents research on why false alarms are, and will remain a problem; then applies results from the field of information visualization to the problem of intrusion detection. This approach promises to enable the operator to identify false (and true) alarms, while aiding the operator to identify other operational characteristics of intrusion detection systems. This volume presents four different visualization approaches, mainly applied to data from web server access logs. Understanding Intrusion Detection through Visualization is structured for security professionals, researchers and practitioners. This book is also suitable for graduate students in computer science.
650		0	_aCOMPUTER SCIENCE.
650		0	_aCOMPUTER COMMUNICATION NETWORKS.
650		0	_aDATA STRUCTURES (COMPUTER SCIENCE).
650		0	_aDATA ENCRYPTION (COMPUTER SCIENCE).
650		0	_aCOMPUTER VISION.
650		0	_aOPTICAL PATTERN RECOGNITION.
650	1	4	_aCOMPUTER SCIENCE.
650	2	4	_aDATA ENCRYPTION.
650	2	4	_aCOMPUTER IMAGING, VISION, PATTERN RECOGNITION AND GRAPHICS.
650	2	4	_aPATTERN RECOGNITION.
650	2	4	_aDATA STRUCTURES, CRYPTOLOGY AND INFORMATION THEORY.
650	2	4	_aCOMPUTER COMMUNICATION NETWORKS.
700	1		_aSands, David. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387276342
830		0	_aAdvances in Information Security, _x1568-2633 ; _v24
856	4	0	_uhttp://dx.doi.org/10.1007/0-387-27636-X _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SCS
942			_2ddc _cER
999			_c56719 _d56719