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

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.

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