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

Book Description

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. This authoritative source collects must-read research for network administrators and researchers in related fields.

Table of Contents

  1. Copyright
  2. Dedication
  3. Preface
  4. Acknowledgment
  5. Foundations
    1. Statistical Opportunities, Roles, and Challenges in Network Security
      1. INTRODUCTION
      2. OVERVIEW
      3. STATISTICAL APROACHES IN PRACTICE
      4. FUNDAMENTAL STATISTICAL ROLES AND CHALLENGES IN NETWORK SECURITY
      5. SUMMARY
    2. REFERENCES
      1. ENDNOTES
    3. Statistical Analysis Software
      1. INTRODUCTION
      2. THE SAS SYSTEM
      3. STATA
      4. R
      5. OTHER PACKAGES
      6. MATLAB
      7. SUMMARY
    4. REFERENCES
      1. ENDNOTES
    5. Network Traffic and Data
      1. INTRODUCTION
      2. SYSTEM-SPECIFIC DATA
      3. USER-SPECIFIC DATA
      4. PUBLICLY AVAILABLE DATA
      5. SUMMARY
    6. REFERENCES
      1. ENDNOTES
      2. APPENDIX
    7. Network Data Characteristics
      1. INTRODUCTION
      2. RANDOM VARIABLES
      3. VARIABLE DISTRIBUTIONS
      4. NETWORK DATA MODULES
      5. SUMMARY
    8. REFERENCES
  6. Data Mining and Modeling
    1. Exploring Network Data
      1. INTRODUCTION
      2. DESCRIPTIVE ANALYSIS
      3. VISUALIZING ANALYSIS
      4. DATA TRANSFORMATION
      5. SUMMARY
    2. REFERENCES
      1. APPENDIX
    3. Data Reduction
      1. INTRODUCTION
      2. DATA STRUCTURE DETECTION
      3. SAMPLING NETWORK TRAFFIC
      4. SAMPLE SIZE
      5. SUMMARY
    4. REFERENCES
      1. APPENDIX
    5. Models Network Data for Association and Prediction
      1. INTRODUCTION
      2. BIVARIATE ANALYSIS
      3. LINEAR REGRESSION MODELING
      4. ROBUSTNESS ASOCIATION
      5. SUMMARY
    6. REFERENCES
      1. APENDIX
    7. Measuring User Behavior
      1. INTRODUCTION
      2. USER BEHAVIOR PATTERN
      3. SCORING METHODS
      4. PROFILING MODELS
      5. SUMMARY
    8. REFERENCES
      1. APENDIX
  7. Classifications, Profiles, and Making Better Decisions
    1. Classification Based on Supervised Learning
      1. INTRODUCTION
      2. GENERALIZED LINEAR METHODS
      3. NONPARAMETRIC METHODS
      4. OTHER LINEAR AND NONLINEAR METHODS
      5. SUMMARY
    2. REFERENCES
      1. ENDNOTE
    3. Classification Based on Unsupervised Learning
      1. INTRODUCTION
      2. PROBABILITY MODELS
      3. SIMILARITY MODELS
      4. MULTIDIMENSIONAL MODELS
      5. SUMMARY
    4. REFERENCES
      1. APENDIX
    5. Decision Analysis in Network Security
      1. INTRODUCTION
      2. ANALYSIS OF UNCERTAINTY
      3. STATISTICAL CONTROL CHART
      4. RANKING
      5. SUMMARY
    6. REFERENCES
      1. APENDIX
    7. Evaluation
      1. INTRODUCTION
      2. DATA RELIABILITY, VALIDITY, AND QUALITY
      3. GOODNESS OF CLASSIFICATION
      4. ASSESS MODEL PERFORMANCE
      5. SUMMARY
    8. REFERENCES
  8. About the Author
  9. Index