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Machine Learning Approaches in Cyber Security Analytics / by Tony Thomas, Athira P. Vijayaraghavan, Sabu Emmanuel.
- Format:
- Book
- Author/Creator:
- Thomas, Tony, author.
- P. Vijayaraghavan, Athira, author.
- Emmanuel, Sabu, author.
- Series:
- Computer Science (Springer-11645)
- Language:
- English
- Subjects (All):
- Computer security.
- Application software.
- Data encryption (Computer science).
- Computer crimes.
- Data structures (Computer science).
- Systems and Data Security.
- Information Systems Applications (incl. Internet).
- Cryptology.
- Cybercrime.
- Data Structures.
- Local Subjects:
- Systems and Data Security.
- Information Systems Applications (incl. Internet).
- Cryptology.
- Cybercrime.
- Data Structures.
- Physical Description:
- 1 online resource (XI, 209 pages) : 76 illustrations, 43 illustrations in color
- Edition:
- First edition 2020.
- Contained In:
- Springer eBooks
- Place of Publication:
- Singapore : Springer Singapore : Imprint: Springer, 2020.
- System Details:
- text file PDF
- Summary:
- 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. .
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-981-15-1706-8
- 9789811517068
- 9789811517051
- 9789811517075
- 9789811517082
- Access Restriction:
- Restricted for use by site license.
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