About this ebook
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.
About the author
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. [Editor]
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