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006			m o d
007			cr cn
008			110525s2011 flua ob 001 0 eng d
020			_a9780429063756
035			_a(OCoLC)727798816
040			_aFlBoTFG _cFlBoTFG
082			_a005.8 _bD812
090			_aQA76.9.D343 _bD825 2011
100	1		_aDua, Sumeet and Du, Xian
245	1	0	_aData Mining and Machine Learning in Cybersecurity
260			_aBoca Raton : _bCRC Press, _cc2011.
300			_a256 p. : _b69 B/W ill.
500			_aAn Auerbach book.
504			_aIncludes bibliographical references and index.
505	0		_a1. Introduction -- 2. Classical machine-learning paradigms for data mining -- 3. Supervised learning for misuse/signature detection -- 4. Machine learning for anomaly detection -- 5. Machine learning for hybrid detection -- 6. Machine learning for scan detection -- 7. Machine learning for profiling network traffic -- 8. Privacy-preserving data mining -- 9. Emerging challenges in cybersecurity.
520			_aIntroducing basic concepts of machine learning and data mining methodologies for cyber security, this book provides a unified reference for specific machine learning solutions and cybersecurity problems. The authors focus on how to apply machine learning methodologies in cybersecurity, categorizing methods for detecting, scanning, profiling, intrusions, and anomalies. The text presents challenges and solutions in machine learning along with cybersecurity fundamentals. It also describes advanced problems in cybersecurity in the machine learning domain and examines privacy-preserving data mining methods as a proactive security solution-- _cProvided by publisher.
530			_aAlso available in print edition.
538			_aMode of access: World Wide Web.
650		0	_aData mining.
650		0	_aMachine learning.
650		0	_aComputer security.
655		7	_aElectronic books. _2lcsh
776	1		_z9781439839423
856	4	0	_uhttp://marc.crcnetbase.com/isbn/9781439839430 _qebook
942			_2ddc _cEBK
999			_c1020 _d1020