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Modern Statistically-Based Intrusion Detection and Protection

By: Yun Wang

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Hardcover | 31 October 2008

At a Glance

Hardcover
476 Pages

Dimensions(cm)
25.4 x 17.78 x 3.18

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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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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Networking & Communications Network Security Computer Security Data Encryption Economics, Finance, Business and Management Computer Science Artificial Intelligence

Preface	p. viii
Acknowledgment	p. xi
Foundations
Statistical Opportunities, Roles, and Challenges in Network Security	p. 1
Introduction	p. 1
Overview	p. 2
Statistical Approaches in Practice	p. 14
Fundamental Statistical Roles and Challenges in Network Security	p. 22
Summary	p. 27
References	p. 27
Endnotes	p. 34
Statistical Analysis Software	p. 35
Introduction	p. 35
The SAS System	p. 37
STATA	p. 45
R	p. 48
Other Packages	p. 54
Summary	p. 58
References	p. 59
Endnotes	p. 59
Network Traffic and Data	p. 60
Introduction	p. 60
System-Specific Traffic	p. 60
User-Specific Data	p. 65
Publicly Available Data	p. 70
Summary	p. 91
References	p. 92
Endnotes	p. 94
Appendix	p. 95
Network Data Characteristics	p. 104
Introduction	p. 104
Random Variables	p. 105
Variable Distributions	p. 109
Network Data Modules	p. 116
Summary	p. 121
References	p. 121
Data Mining and Modeling
Exploring Network Data	p. 124
Introduction	p. 124
Descriptive Analysis	p. 125
Visualizing Analysis	p. 134
Data Transformation	p. 144
Summary	p. 155
References	p. 156
Appendix	p. 157
Data Reduction	p. 172
Introduction	p. 172
Data Structure Detection	p. 173
Sampling Network Traffic	p. 188
Sample Size	p. 199
Summary	p. 206
References	p. 206
Appendix	p. 210
Models Network Data for Association and Prediction	p. 220
Introduction	p. 220
Bivariate Analysis	p. 221
Linear Regression Modeling	p. 232
Robustness Association	p. 249
Summary	p. 257
References	p. 257
Appendix	p. 260
Measuring User Behavior	p. 261
Introduction	p. 261
User Behavior Pattern	p. 262
Scoring Methods	p. 276
Profiling Models	p. 286
Summary	p. 297
References	p. 297
Appendix	p. 301
Classifications, Profiles, and Making Better Decisions
Classification Based on Supervised Learning	p. 305
Introduction	p. 305
Generalized Linear Methods	p. 306
Nonparametric Methods	p. 316
Other Linear and Nonlinear Methods	p. 333
Summary	p. 342
References	p. 343
Endnote	p. 347
Classification Based on Unsupervised Learning	p. 348
Introduction	p. 348
Probability Models	p. 349
Similarity Models	p. 365
Multidimensional Models	p. 379
Summary	p. 390
References	p. 392
Appendix	p. 395
Decision Analysis in Network Security	p. 396
Introduction	p. 396
Analysis of Uncertainty	p. 398
Statistical Control Chart	p. 411
Ranking	p. 415
Summary	p. 422
References	p. 423
Appendix	p. 425
Evaluation	p. 427
Introduction	p. 427
Data Reliability, Validity, and Quality	p. 428
Goodness of Classification	p. 435
Assess Model Performance	p. 447
Summary	p. 455
References	p. 456
About the Author	p. 458
Index	p. 459
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Modern Statistically-Based Intrusion Detection and Protection

At a Glance

Hardcover

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