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

Statistical Techniques for Network Security: Modern Statistically-Based Intrusion Detection and Protection by Yun Wang, published by Information Science Reference in 2009, offers a comprehensive exploration of intrusion detection and protection within the realm of computer and network security. This edition spans 461 pages and is presented in English, focusing on the application of statistical modeling techniques to enhance security measures against anomalies in network traffic.
Readers will find an in-depth discussion on various topics, including anomaly intrusion detection and predictive event modeling, which are essential for network administrators and security professionals. The book serves as a valuable resource for those engaged in the fields of statistics, computer science, and network security, providing insights into the common use of statistically-based methods for addressing intrusion detection challenges.
Official synopsis Publisher
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.
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