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Machine Learning Approaches in Cyber Security Analytics [electronic resource] /

By: Contributor(s): Material type: TextTextPublisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2020Edition: 1st ed. 2020Description: XI, 209 p. 76 illus., 43 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9789811517068
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 005.8 23
LOC classification:
  • QA76.9.A25
Online resources:
Contents:
Chapter 1. Introduction -- Chapter 2. Machine Learning Algorithms -- Chapter 3. Machine Learning in Cyber Security Analytics -- Chapter 4. Applications of Support Vector Machines -- Chapter 5. Applications of Nearest Neighbor -- Chapter 6. Applications of Clustering -- Chapter 7. Applications of Dimensionality Reduction -- Chapter 8. Applications of other Machine Learning Methods.
In: Springer Nature eBookSummary: This book introduces various machine learning methods for cyber security analytics. With an overwhelming amount of data being generated and transferred over various networks, monitoring everything that is exchanged and identifying potential cyber threats and attacks poses a serious challenge for cyber experts. Further, as cyber attacks become more frequent and sophisticated, there is a requirement for machines to predict, detect, and identify them more rapidly. Machine learning offers various tools and techniques to automate and quickly predict, detect, and identify cyber attacks. .
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Chapter 1. Introduction -- Chapter 2. Machine Learning Algorithms -- Chapter 3. Machine Learning in Cyber Security Analytics -- Chapter 4. Applications of Support Vector Machines -- Chapter 5. Applications of Nearest Neighbor -- Chapter 6. Applications of Clustering -- Chapter 7. Applications of Dimensionality Reduction -- Chapter 8. Applications of other Machine Learning Methods.

This book introduces various machine learning methods for cyber security analytics. With an overwhelming amount of data being generated and transferred over various networks, monitoring everything that is exchanged and identifying potential cyber threats and attacks poses a serious challenge for cyber experts. Further, as cyber attacks become more frequent and sophisticated, there is a requirement for machines to predict, detect, and identify them more rapidly. Machine learning offers various tools and techniques to automate and quickly predict, detect, and identify cyber attacks. .

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