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Data Mining and Machine Learning in Cybersecurity

By: (Author) Sumeet Dua , (Author) Xian Du

Extended Catalogue

Ksh 16,200.00

Format: Hardback or Cased Book

ISBN-10: 1439839425

ISBN-13: 9781439839423

Publisher: Taylor & Francis Inc

Imprint: Taylor & Francis Inc

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Apr 25th, 2011

Print length: 256 Pages

Weight: 516 grams

Dimensions (height x width x thickness): 24.10 x 15.90 x 2.40 cms

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  • Description

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From basic concepts in machine learning and data mining to advanced problems in the machine learning domain, this book provides a unified reference for specific machine learning solutions to cybersecurity problems. It supplies a foundation in cybersecurity fundamentals, surveys contemporary challenges, and details cutting-edge machine learning and data mining techniques. This interdisciplinary resource includes technique review tables that allow for speedy access to common cybersecurity problems and associated data mining methods. Numerous illustrative figures and more than 40 case studies help readers visualize the workflow of complex techniques.

With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity. Although several conferences, workshops, and journals focus on the fragmented research topics in this area, there has been no single interdisciplinary resource on past and current works and possible paths for future research in this area. This book fills this need.

From basic concepts in machine learning and data mining to advanced problems in the machine learning domain, Data Mining and Machine Learning in Cybersecurity provides a unified reference for specific machine learning solutions to cybersecurity problems. It supplies a foundation in cybersecurity fundamentals and surveys contemporary challenges—detailing cutting-edge machine learning and data mining techniques. It also:

  • Unveils cutting-edge techniques for detecting new attacks
  • Contains in-depth discussions of machine learning solutions to detection problems
  • Categorizes methods for detecting, scanning, and profiling intrusions and anomalies
  • Surveys contemporary cybersecurity problems and unveils state-of-the-art machine learning and data mining solutions
  • Details privacy-preserving data mining methods

This interdisciplinary resource includes technique review tables that allow for speedy access to common cybersecurity problems and associated data mining methods. Numerous illustrative figures help readers visualize the workflow of complex techniques and more than forty case studies provide a clear understanding of the design and application of data mining and machine learning techniques in cybersecurity.


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