Secure Computer and Network Systems Modeling, Analysis and Design

Note: Supplemental materials are not guaranteed with Rental or Used book purchases.

ISBN: 9780470023242 | 0470023244
Cover: Hardcover
Copyright: 2/26/2008

Buy New

Print on Demand: 2-4 Weeks. This item cannot be cancelled or returned.

$162.41
eBook

Available Instantly

Online: 1825 Days

Downloadable: Lifetime Access

*To support the delivery of the digital material to you, a digital delivery fee of $3.99 will be charged on each digital item.

$147.60*

This book presents a wide range of modelling, analysis and assurance techniques that address computer network dependability, security and quality of service (QoS). In-depth coverage of technical material is supported with experimental data and numerical examples to illustrate why and how the technologies work, making the book useful as a text for graduate students. Since the next-generation of computer network systems and information infrastructure rely on the scientific and engineering approaches to provide security, QoS and ultimately system dependability, this book will benefit many people in academia and industry working to achieve this goal.

Preface	p. xi
An Overview of Computer and Network Security
Assets, vulnerabilities and threats of computer and network systems	p. 3
Risk assessment	p. 3
Assets and asset attributes	p. 4
Resource, process and user assets and their interactions	p. 5
Cause-effect chain of activity, state and performance	p. 6
Asset attributes	p. 8
Vulnerabilities	p. 11
Boundary condition error	p. 12
Access validation error and origin validation error	p. 12
Input validation error	p. 13
Failure to handle exceptional conditions	p. 13
Synchronization errors	p. 13
Environment error	p. 13
Configuration error	p. 14
Design error	p. 14
Unknown error	p. 15
Threats	p. 15
Objective, origin, speed and means of threats	p. 15
Attack stages	p. 21
Asset risk framework	p. 21
Summary	p. 22
References	p. 23
Protection of computer and network systems	p. 25
Cyber attack prevention	p. 25
Access and flow control	p. 25
Secure computer and network design	p. 29
Cyber attack detection	p. 29
Data, events and incidents	p. 30
Detection	p. 31
Assessment	p. 32
Cyber attack response	p. 32
Summary	p. 33
References	p. 33
Secure System Architecture and Design
Asset protection-driven, policy-based security protection architecture	p. 39
Limitations of a threat-driven security protection paradigm	p. 39
A new, asset protection-driven paradigm of security protection	p. 40
Data to monitor: assets and asset attributes	p. 41
Events to detect: mismatches of asset attributes	p. 41
Incidents to analyze and respond: cause-effect chains of mismatch events	p. 42
Proactive asset protection against vulnerabilities	p. 42
Digital security policies and policy-based security protection	p. 43
Digital security policies	p. 43
Policy-based security protection	p. 45
Enabling architecture and methodology	p. 46
An Asset Protection Driven Security Architecture (APDSA)	p. 46
An Inside-Out and Outside-In (IOOI) methodology of gaining knowledge about data, events and incidents	p. 47
Further research issues	p. 48
Technologies of asset attribute data acquisition	p. 48
Quantitative measures of asset attribute data and mismatch events	p. 48
Technologies for automated monitoring, detection, analysis and control of data, events, incidents and COA	p. 49
Summary	p. 49
References	p. 50
Job admission control for service stability	p. 53
A token bucket method of admission control in DiffServ and InteServ models	p. 53
Batch Scheduled Admission Control (BSAC) for service stability	p. 55
Service stability in service reservation for instantaneous jobs	p. 56
Description of BSAC	p. 57
Performance advantage of the BSAC router model over a regular router model	p. 60
Summary	p. 64
References	p. 64
Job scheduling methods for service differentiation and service stability	p. 65
Job scheduling methods for service differentiation	p. 65
Weighted Shortest Processing Time (WSPT), Earliest Due Date (EDD) and Simplified Apparent Tardiness Cost (SATC)	p. 65
Comparison of WSPT, ATC and EDD with FIFO in the best effort model and in the DiffServ model in service differentiation	p. 66
Job scheduling methods for service stability	p. 70
Weighted Shortest Processing Time - Adjusted (WSPT-A) and its performance in service stability	p. 70
Verified Spiral (VS) and Balanced Spiral (BS) methods for a single service resource and their performance in service stability	p. 73
Dynamics Verified Spiral (DVS) and Dynamic Balanced Spiral (DBS) methods for parallel identical resources and their performance in service stability	p. 78
Summary	p. 79
References	p. 79
Job reservation and service protocols for end-to-end delay guarantee	p. 81
Job reservation and service in InteServ and RSVP	p. 81
Job reservation and service in I-RSVP	p. 82
Job reservation and service in SI-RSVP	p. 86
Service performance of I-RSVP and SI-RSVP in comparison with the best effort model	p. 89
The simulation of a small-scale computer network with I-RSVP, SI-RSVP and the best effort model	p. 89
The simulation of a large-scale computer network with I-RSVP, SI-RSVP and the best effort model	p. 91
Service performance of I-RSVP, SI-RSVP and the best effort model	p. 93
Summary	p. 102
References	p. 103
Mathematical/Statistical Features and Characteristics of Attack and Normal Use Data
Collection of Windows performance objects data under attack and normal use conditions	p. 107
Windows performance objects data	p. 107
Description of attacks and normal use activities	p. 111
Apache Resource DoS	p. 111
ARP Poison	p. 111
Distributed DoS	p. 112
Fork Bomb	p. 113
FTP Buffer Overflow	p. 113
Hardware Keylogger	p. 113
Remote Dictionary	p. 113
Rootkit	p. 113
Security Audit	p. 114
Software Keylogger	p. 114
Vulnerability Scan	p. 114
Text Editing	p. 114
Web Browsing	p. 114
Computer network setup for data collection	p. 115
Procedure of data collection	p. 115
Summary	p. 118
References	p. 118
Mean shift characteristics of attack and normal use data	p. 119
The mean feature of data and two-sample test of mean difference	p. 119
Data pre-processing	p. 121
Discovering mean shift data characteristics for attacks	p. 121
Mean shift attack characteristics	p. 122
Examples of mean shift attack characteristics	p. 122
Mean shift attack characteristics by attacks and windows performance objects	p. 124
Attack groupings based on the same and opposite attack characteristics	p. 128
Unique attack characteristics	p. 136
Summary	p. 139
References	p. 139
Probability distribution change characteristics of attack and normal use data	p. 141
Observation of data patterns	p. 141
Skewness and mode tests to identify five types of probability distributions	p. 146
Procedure for discovering probability distribution change data characteristics for attacks	p. 148
Distribution change attack characteristics	p. 150
Percentages of the probability distributions under the attack and normal use conditions	p. 150
Examples of distribution change attack characteristics	p. 151
Distribution change attack characteristics by attacks and Windows performance objects	p. 151
Attack groupings based on the same and opposite attack characteristics	p. 161
Unique attack characteristics	p. 167
Summary	p. 173
References	p. 174
Autocorrelation change characteristics of attack and normal use data	p. 175
The autocorrelation feature of data	p. 175
Discovering the autocorrelation change characteristics for attacks	p. 176
Autocorrelation change attack characteristics	p. 178
Percentages of variables with three autocorrelation levels under the attack and normal use conditions	p. 178
Examples of autocorrelation change attack characteristics	p. 179
Autocorrelation change attack characteristics by attacks and Windows performance objects	p. 182
Attack groupings based on the same and opposite attack characteristics	p. 182
Unique attack characteristics	p. 193
Summary	p. 193
References	p. 196
Wavelet change characteristics of attack and normal use data	p. 197
The wavelet feature of data	p. 197
Discovering the wavelet change characteristics for attacks	p. 201
Wave change attack characteristics	p. 203
Examples of wavelet change attack characteristics	p. 203
Wavelet change attack characteristics by attacks and Windows performance objects	p. 204
Attack groupings based on the same and opposite attack characteristics	p. 222
Unique attack characteristics	p. 225
Summary	p. 243
References	p. 243
Cyber Attack Detection: Signature Recognition
Clustering and classifying attack and normal use data	p. 247
Clustering and Classification Algorithm - Supervised (CCAS)	p. 248
Training and testing data	p. 251
Application of CCAS to cyber attack detection	p. 251
Detection performance of CCAS	p. 253
Summary	p. 256
References	p. 256
Learning and recognizing attack signatures using artificial neural networks	p. 257
The structure and back-propagation learning algorithm of feedforward ANNs	p. 257
The ANN application to cyber attack detection	p. 260
Summary	p. 270
References	p. 271
Cyber Attack Detection: Anomaly Detection
Statistical anomaly detection with univariate and multivariate data	p. 275
EWMA control charts	p. 275
Application of the EWMA control chart to cyber attack detection	p. 277
Chi-Square Distance Monitoring (CSDM) method	p. 284
Application of the CSDM method to cyber attack detection	p. 286
Summary	p. 288
References	p. 288
Stochastic anomaly detection using the Markov chain model of event transitions	p. 291
The Markov chain model of event transitions for cyber attack detection	p. 291
Detection performance of the Markov chain model-based anomaly detection technique and performance degradation with the increased mixture of attack and normal use data	p. 293
Summary	p. 295
References	p. 296
Cyber Attack Detection: Attack Norm Separation
Mathematical and statistical models of attack data and normal use data	p. 299
The training data for data modeling	p. 299
Statistical data models for the mean feature	p. 300
Statistical data models for the distribution feature	p. 300
Time-series based statistical data models for the autocorrelation feature	p. 301
The wavelet-based mathematical model for the wavelet feature	p. 304
Summary	p. 309
References	p. 312
Cuscore-based attack norm separation models	p. 313
The cuscore	p. 313
Application of the cuscore models to cyber attack detection	p. 314
Detection performance of the cuscore detection models	p. 316
Summary	p. 323
References	p. 325
Security Incident Assessment
Optimal selection and correlation of attack data characteristics in attack profiles	p. 329
Integer programming to select an optimal set of attack data characteristics	p. 329
Attack profiling	p. 330
Summary	p. 332
References	p. 332
Index	p. 333
Table of Contents provided by Ingram. All Rights Reserved.

Please wait while the item is added to your bag...

FREE SHIPPING

Rent More, Save More! Use code: BBRENTAL

5% off 1 book, 7% off 2 books, 10% off 3+ books

Secure Computer and Network Systems Modeling, Analysis and Design

Summary

Author Biography

Table of Contents

Supplemental Materials

FREE SHIPPING

Rent More, Save More! Use code: BBRENTAL

5% off 1 book, 7% off 2 books, 10% off 3+ books

Secure Computer and Network Systems Modeling, Analysis and Design

Summary

Author Biography

Table of Contents

Supplemental Materials

Digital License