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008 110525s2011 flua sb 001 0 eng d
020 _a9781439839430 (ebook : PDF)
040 _aFlBoTFG
_cFlBoTFG
090 _aQA76.9.D343
_bD825 2011
092 _a005.8
_bD812
100 1 _aDua, Sumeet.
245 1 0 _aData mining and machine learning in cybersecurity
_h[electronic resource] /
_cSumeet Dua and Xian Du.
260 _aBoca Raton :
_bTaylor & Francis,
_c2011.
300 _axxii, 234 p. :
_bill.
500 _a"An 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 _a"Introducing 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
700 1 _aDu, Xian.
776 1 _z9781439839423
856 4 0 _uhttp://marc.crcnetbase.com/isbn/9781439839430
_qapplication/PDF
_zDistributed by publisher. Purchase or institutional license may be required for access.
999 _c15713
_d15713