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.
Author(s): Yun Wang
Series: Premier Reference Source
Publisher: IGI Global
Year: 2008
Language: English
Pages: 476
Title......Page 2
Table of Contents......Page 5
Preface......Page 9
Acknowledgment......Page 12
Statistical Opportunities, Roles, and Challenges in Network Security......Page 16
Statistical Analysis Software......Page 50
Network Traffic and Data......Page 75
Network Data Characteristics......Page 119
Exploring Network Data......Page 139
Data Reduction......Page 187
Models Network Data for Association and Prediction......Page 235
Measuring User Behavior......Page 276
Classification Based on Supervised Learning......Page 320
Classification Based on Unsupervised Learning......Page 363
Decision Analysis in Network Security......Page 411
Evaluation......Page 442
About the Author......Page 473
Index......Page 474
