Statistical Techniques for Network Security: Modern Statistically-Based Intrusion Detection and Protection

Statistical Techniques for Network Security: Modern Statistically-Based Intrusion Detection and Protection

Yun Wang (Yale-New Haven Health, USA)
Indexed In: SCOPUS View 1 More Indices
Release Date: October, 2008|Copyright: © 2009 |Pages: 476
DOI: 10.4018/978-1-59904-708-9
ISBN13: 9781599047089|ISBN10: 159904708X|EISBN13: 9781599047102
Hardcover:
Available
$165.00
TOTAL SAVINGS: $165.00
Benefits
  • Printed-On-Demand (POD)
  • Usually ships one day from order
Hardcover:
Available
$165.00
TOTAL SAVINGS: $165.00
Benefits
  • Printed-On-Demand (POD)
  • Usually ships one day from order
E-Book:
Available
$165.00
TOTAL SAVINGS: $165.00
Benefits
  • Multi-user license (no added fee)
  • Immediate access after purchase
  • No DRM
  • PDF download
E-Book:
Available
$165.00
TOTAL SAVINGS: $165.00
Benefits
  • Immediate access after purchase
  • No DRM
  • PDF download
  • Receive a 10% Discount on eBooks
Hardcover +
E-Book:
Available
$195.00
TOTAL SAVINGS: $195.00
Benefits
  • Printed-On-Demand (POD)
  • Usually ships one day from order
  • Multi-user license (no added fee)
  • Immediate access after purchase
  • No DRM
  • PDF download
Hardcover +
E-Book:
Available
$195.00
TOTAL SAVINGS: $195.00
Benefits
  • Printed-On-Demand (POD)
  • Usually ships one day from order
  • Immediate access after purchase
  • No DRM
  • PDF download
OnDemand:
(Individual Chapters)
Available
$29.50
TOTAL SAVINGS: $29.50
Benefits
  • Purchase individual chapters from this book
  • Immediate PDF download after purchase or access through your personal library
Description & Coverage
Description:

Intrusion detection and protection is a key component in the framework of the computer and network security area. Although various classification algorithms and approaches have been developed and proposed over the last decade, the statistically-based method remains the most common approach to anomaly intrusion detection.

Statistical Techniques for Network Security: Modern Statistically-Based Intrusion Detection and Protection bridges between applied statistical modeling techniques and network security to provide statistical modeling and simulating approaches to address the needs for intrusion detection and protection. Covering in-depth topics such as network traffic data, anomaly intrusion detection, and prediction events, this authoritative source collects must-read research for network administrators, information and network security professionals, statistics and computer science learners, and researchers in related fields.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Data mining and modeling
  • Data reduction techniques for network traffic
  • Data reliability, validity, and quality
  • Decision analysis in network security
  • Measure user behavior
  • Models network data for association and prediction
  • Network data characteristics
  • Network traffic and data
  • Statistical opportunity in network security
  • Statistical software for analyzing network data
  • Supervised learning techniques for network traffic classification
  • Unsupervised learning techniques for network traffic classification
Reviews & Statements

This book provides a guide for applying modern statistical techniques for intrusion detection and prevention.

– Yun Wang, Yale-New Haven Health

The author includes sections on testing data reliability, validity, and quality as well as assessing model performance within a security network.

– Book News Inc. (March 2009)

This authoritative source collects must-read research for network administrations, information, and network security professionals, statistics and computer science learners, and researchers in related fields. Statistical Techniques for Networked Security is for all academic and research libraries.

– SirReadaLot (April 2009)

This book offers a comprehensive view of statistical approaches for network intrusion detection. The main topic of network intrusion detection is deciding whether an observed event or sequence of events is legitimate or malicious. Over the years, the research community has proposed statistical approaches for accomplishing this, but until now, the proposals were only available in specialized conference proceedings and journals. This book provides a unified and comprehensive introduction to the topic. The context of the book is interdisciplinary, linking expertise from computer networks, operating systems, and statistics.

– Radu State, University of Luxembourg, Computing Reviews
Table of Contents
Search this Book:
Reset
Editor/Author Biographies
Yun Wang, PhD, is a senior biostatistician and information specialist at the Center for Outcomes Research and Evaluation, Yale University and Yale-New Haven Health System, and a consultant at Qualidigm. He has degrees in mathematics, computer science, information system, and criminal law with concentration in criminal statistics. His research interests include developing large complex information systems and applying statistical modeling techniques for information analyses, information security, and patient private protection.
Abstracting & Indexing
Archiving
All of IGI Global's content is archived via the CLOCKSS and LOCKSS initiative. Additionally, all IGI Global published content is available in IGI Global's InfoSci® platform.