Machine Learning in Cyber Trust

Security, Privacy, and Reliability

Editors: Tsai, Jeffrey J. P., Yu, Philip S. (Eds.)

  • Provides the reader with an overview of machine-learning methods
  • Demonstrates how machine learning is used to deal with the security, reliability, performance, and privacy of cyber-based systems
  • Presents the state of the practice in machine learning and cyber systems and identifies further efforts needed to produce fruitful results
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eBook $139.00
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  • ISBN 978-0-387-88735-7
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  • Download immediately after purchase
Hardcover $179.00
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  • ISBN 978-0-387-88734-0
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  • Online orders shipping within 2-3 days.
Softcover $179.00
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  • ISBN 978-1-4419-4698-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

Many networked computer systems are far too vulnerable to cyber attacks that can inhibit their functioning, corrupt important data, or expose private information. Not surprisingly, the field of cyber-based systems turns out to be a fertile ground where many tasks can be formulated as learning problems and approached in terms of machine learning algorithms.

This book contains original materials by leading researchers in the area and covers applications of different machine learning methods in the security, privacy, and reliability issues of cyber space. It enables readers to discover what types of learning methods are at their disposal, summarizing the state of the practice in this important area, and giving a classification of existing work.

Specific features include the following:

  • A survey of various approaches using machine learning/data mining techniques to enhance the traditional security mechanisms of databases
  • A discussion of detection of SQL Injection attacks and anomaly detection for defending against insider threats
  • An approach to detecting anomalies in a graph-based representation of the data collected during the monitoring of cyber and other infrastructures
  • An empirical study of seven online-learning methods on the task of detecting malicious executables
  • A novel network intrusion detection framework for mining and detecting sequential intrusion patterns
  • A solution for extending the capabilities of existing systems while simultaneously maintaining the stability of the current systems
  • An image encryption algorithm based on a chaotic cellular neural network to deal with information security and assurance
  • An overview of data privacy research, examining the achievements, challenges and opportunities while pinpointing individual research efforts on the grand map of data privacy protection
  • An algorithm based on secure multiparty computation primitives to compute the nearest neighbors of records in horizontally distributed data
  • An approach for assessing the reliability of SOA-based systems using AI reasoning techniques
  • The models, properties, and applications of context-aware Web services, including an ontology-based context model to enable formal description and acquisition of contextual information pertaining to service requestors and services

Those working in the field of cyber-based systems, including industrial managers, researchers, engineers, and graduate and senior undergraduate students will find this an indispensable guide in creating systems resistant to and tolerant of cyber attacks.

Reviews

From the reviews:

"This is a useful book on machine learning for cyber security applications. It will be helpful to researchers and graduate students who are looking for an introduction to a specific topic in the field. All of the topics covered are well researched. The book consists of 12 chapters, grouped into four parts." (Imad H. Elhajj, ACM Computing Reviews, October, 2009)


Table of contents (12 chapters)

  • Cyber-Physical Systems: A New Frontier

    Sha, Lui (et al.)

    Pages 3-13

  • Misleading Learners: Co-opting Your Spam Filter

    Nelson, Blaine (et al.)

    Pages 17-51

  • Survey of Machine Learning Methods for Database Security

    Kamra, Ashish (et al.)

    Pages 53-71

  • Identifying Threats Using Graph-based Anomaly Detection

    Eberle, William (et al.)

    Pages 73-108

  • On the Performance of Online Learning Methods for Detecting Malicious Executables

    Maloof, Marcus A.

    Pages 109-132

Buy this book

eBook $139.00
price for USA
  • ISBN 978-0-387-88735-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Hardcover $179.00
price for USA
  • ISBN 978-0-387-88734-0
  • Free shipping for individuals worldwide
  • Online orders shipping within 2-3 days.
Softcover $179.00
price for USA
  • ISBN 978-1-4419-4698-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.

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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning in Cyber Trust
Book Subtitle
Security, Privacy, and Reliability
Editors
  • Jeffrey J. P. Tsai
  • Philip S. Yu
Copyright
2009
Publisher
Springer US
Copyright Holder
Springer-Verlag US
eBook ISBN
978-0-387-88735-7
DOI
10.1007/978-0-387-88735-7
Hardcover ISBN
978-0-387-88734-0
Softcover ISBN
978-1-4419-4698-0
Edition Number
1
Number of Pages
XVI, 362
Number of Illustrations and Tables
100 b/w illustrations
Topics