Statistical Techniques For Network Security: Modern Statistically-based Intrusion Detection And Protection (premier Reference Source)
by Yun Wang /
2008 / English / PDF
9.5 MB Download
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.
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.
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.