Update cookies preferences

E-book: Trustworthy Computing: Analytical and Quantitative Engineering Evaluation illustrated edition [Wiley Online]

  • Format: 320 pages, Illustrations, Contains 1 CD-ROM
  • Pub. Date: 01-Aug-2007
  • Publisher: Wiley-Blackwell
  • ISBN-10: 470127872
  • ISBN-13: 9780470127872
Other books in subject:
  • Wiley Online
  • Price: 170,49 €*
  • * this price gives unlimited concurrent access for unlimited time
Trustworthy Computing: Analytical and Quantitative Engineering Evaluation illustrated editionLarger Image
  • Format: 320 pages, Illustrations, Contains 1 CD-ROM
  • Pub. Date: 01-Aug-2007
  • Publisher: Wiley-Blackwell
  • ISBN-10: 470127872
  • ISBN-13: 9780470127872
Other books in subject:
Wiley Online E-booksMore info about Wiley Online
Companion website: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470127872
It would seem the title is an oxymoron but author Sahinoglu (computer science, Troy U.) really means it and proves that theory by taking a quantitative approach to advances in reliability and security engineering. He gives readers metrics to quantify risk and mitigate it through risk management. He describes the fundamentals if component and system reliability and reviews the concept of software reliability, then explains software reliability with clustered failure data and stochastic measures to compare the predictive accuracy of failure-count models, quantitative modeling for security risk assessment, stepping rules on software testing, availability modeling using the Sahinoglu-Libby probability distribution function, and reliability block diagramming in complex systems. The result is an extremely well-organized and logical approach to designing reliability into system rather than on top of them. Annotation ©2007 Book News, Inc., Portland, OR (booknews.com)

"The book itself is a commendable achievement, and it deals with the security and software reliability theory in an integrated fashion with emphasis on practical applications to software engineering and information technology. It is an excellent and unique book and definitely a seminal contribution and first of its kind."

—— C. V. Ramamoorthy

Professor Emeritus, Electrical Engineering and Computer Science, University of California-Berkeley, and Senior Research Fellow, ICC Institute, The University of Texas-Austin, IEEE Life Fellow

Trustworthy Computing: Analytical and Quantitative Engineering Evaluation

presents an index-based, quantitative approach to advances in reliability and security engineering. Objective, metric-oriented, and data-driven, its goal is to establish metrics to quantify risk and mitigate risk through risk management. Based on the author's class-tested curriculum, it covers:

Fundamentals of component and system reliability and a review of software reliability

Software reliability modeling using effort-based and clustered failure data and stochastic comparative measures

Quantitative modeling for security and privacy risk assessment

Cost-effective stopping rules in software reliability testing

Availability modeling using Sahinoglu-Libby (S-L) Probability Distribution

Reliability block diagramming for Simple and Complex Embedded Systems

Complete with a CD-ROM containing case histories and projects that give readers hands-on experience, this is a great text for students in courses on security, reliability, and trustworthiness, as well as a reference for practicing software designers and developers, computer reliability and security specialists, and network administrators who work with data. 

 

Foreword xiii
Preface xvii
Fundamentals of Component and System Reliability and Review of Software Reliability
1(77)
Functions of Importance in Reliability
1(5)
Hazard Rate Functions in Reliability
6(2)
Common Distributions and Random Number Generations
8(25)
Uniform (Rectangular) p.d.f
8(2)
Triangular p.d.f
10(1)
Negative Exponential p.d.f., Pareto, and Power Functions
11(2)
Gamma, Erlang, and Chi-Square p.d.f.'s
13(3)
Student's t-Distribution
16(1)
Fisher's F-Distribution
16(1)
Two- and Three-Parameter (Sahinoglu--Libby) Beta p.d.f.'s
17(3)
Poisson p.m.f.
20(1)
Bernoulli, Binomial, and Multinomial p.m.f.'s
20(1)
Geometric p.m.f.
21(1)
Negative Binomial and Pascal p.m.f.'s
22(1)
Weibull p.d.f.
23(2)
Normal p.d.f.
25(2)
Lognormal p.d.f.
27(1)
Logistic p.d.f.
28(1)
Cauchy p.d.f.
29(1)
Hypergeometric p.m.f.
29(1)
Extreme Value (Gumbel) p.d.f.'s
30(1)
Summary of the Distributions and Relationships Most Commonly Used
31(2)
Life Testing for Component Reliability
33(7)
Estimation Methods for Complete Data
33(3)
Estimation Methods for Incomplete Data
36(4)
Redundancy in System Reliability
40(5)
Series System Reliability
40(1)
Active Parallel Redundancy
41(1)
Standby Redundancy
42(2)
Other Redundancy Limitations: Common-Mode Failures and Load Sharing
44(1)
Review of Software Reliability Growth Models
45(33)
Software Reliability Models in the Time Domain
48(1)
Classification of Reliability Growth Models
49(16)
Appendix 1A: 500 Computer-Generated Random Numbers
65(1)
References
66(5)
Exercises
71(7)
Software Reliability Modeling with Clustered Failure Data and Stochastic Measures to Compare Predictive Accuracy of Failure-Count Models
78(41)
Software Reliability Models Using the Compound Poisson Model
78(21)
Notation and Introduction
79(1)
Background and Motivation
80(1)
Maximum Likelihood Estimation in the Poisson^Geometric Model
81(1)
Nonlinear Regression Estimation in the Poisson^Geometric Model
82(9)
Calculation of Forecast Quality and Comparison of Methods
91(5)
Discussion and Conclusions
96(3)
Stochastic Measures to Compare Failure-Count Reliability Models
99(20)
Introduction and Motivation
99(1)
Definitions and Notation
100(1)
Model, Data, and Computational Formulas
101(3)
Prior Distribution Approach
104(2)
Applications to Data Sets and Computations
106(4)
Discussion and Conclusions
110(3)
References
113(3)
Exercises
116(3)
Quantitative Modeling for Security Risk Assessment
119(53)
Decision Tree Model to Quantify Risk
119(12)
Motivation
119(1)
Risk Scenarios
120(2)
Quantitative Security Meter Model
122(2)
Model Application and Results
124(3)
Modifying the Quantitative Model for Qualitative Data
127(1)
Hybrid Security Meter Model for Both Quantitative and Qualitative Data
127(2)
Simulation Study and Conclusions
129(2)
Bayesian Applications for Prioritizing Software Maintenance
131(7)
Motivation
131(1)
Bayesian Rule in Statistics and Applications for Software Maintenance
132(3)
Another Bayesian Application for Software Maintenance
135(2)
Monte Carlo Simulation to Verify the Bayesian Analysis Proposed
137(1)
Discussion and Conclusions
137(1)
Quantitative Risk Assessment for Nondisjoint Vulnerabilities and Nondisjoint Threats
138(4)
Motivation Behind the Disjoint Notion of Vulnerabilities and Threats
138(1)
Fundamental Probability Laws of Independence, Conditionality, and Disjointness
138(1)
Security Meter Modified for Nondisjoint Vulnerabilities and Disjoint Threats
139(2)
Security Meter Modified for Nondisjoint Vulnerabilities and Nondisjoint Threats
141(1)
Discussion and Conclusions
142(1)
Simple Statistical Design to Estimate the Security Meter Model Input Data
142(8)
Estimating the Input Parameters in the Security Meter Model
143(1)
Statistical Formulas Used to Estimate Inputs in the Security Meter Model
144(1)
Numerical Example of the Statistical Design for the Security Meter Model
145(2)
Discrete Event (Dynamic) Simulation
147(1)
Monte Carlo (Static) Simulation
147(1)
Risk Management Using the Security Meter Model
148(1)
Discussion and Conclusions
149(1)
Statistical Inference to Quantify the Likelihood of Lack of Privacy
150(4)
Introduction: What Is Privacy?
150(1)
How to Quantify Lack of Privacy
151(1)
Numerical Applications for a Privacy Risk Management Study
152(2)
Discussion and Conclusions
154(1)
Appendix 3A: Comparison of Various Risk Assessment Approaches and CINAPEAAA
154(2)
Appendix 3B: Brief Introduction to Encryption, Decryption, and Types
156(3)
Appendix 3C: Attack Trees
159(2)
Appendix 3D: Capabilities-Based Attack Tree Analysis
161(1)
Appendix 3E: Time-to-Defeat Model
162(2)
References
164(3)
Exercises
167(5)
Stopping Rules in Software Testing
172(59)
Effort-Based Empirical Bayesian Stopping Rule
173(32)
Stopping Rule in Test Case--Based (Effort) Models
173(1)
Introduction and Motivation
174(3)
Notation, Compound Poisson Distribution, and Empirical Bayes Estimation
177(5)
Stopping Rule Proposed for Use in Software Testing
182(3)
Applications and Results
185(3)
Discussion and Conclusions
188(3)
Appendix 4A: Analysis Tables
191(2)
Appendix 4B: Comparison of the Proposed CP Rule with Other Stopping Rules
193(7)
Appendix 4C: MESAT-1 Output Screenshots and Graphs
200(5)
Stopping Rule for High-Assurance Software Testing in Business
205(10)
Introduction
205(1)
EVM Methodology
205(1)
Typical SDLC Testing Management
206(1)
New View of Testing
206(2)
Case Study
208(5)
Discussion and Conclusions
213(2)
Bayesian Stopping Rule for Testing in the Time Domain
215(16)
Introduction
215(1)
Review of the Compound Poisson Process
216(1)
Stopping Rule
217(1)
Bayes Analysis for the Poisson^Geometric Model
218(2)
Empirical Bayesian Stopping Rule
220(1)
Computational Example
220(1)
Discussion and Conclusions
221(1)
Appendix 4D: MESAT-2 Applications and Results
221(4)
References
225(4)
Exercises
229(2)
Availability Modeling Using the Sahinoglu--Libby Probability Distribution Function
231(26)
Nomenclature
232(1)
Introduction and Motivation
233(1)
Sahinoglu-Libby Probability Model Formulation
234(1)
Bayes Estimators for Various Informative Priors and Loss Functions
235(4)
Squared-Error Loss Function
236(1)
Absolute-Error Loss Function
236(1)
Weighted Squared-Error Loss Function
237(2)
Availability Calculations for Simple Parallel and Series Networks
239(4)
Discussion and Conclusions
243(14)
Appendix 5A: Derivation of the Sahinoglu--Libby p.d.f.
247(4)
Appendix 5B: Derivation of the Bayes Estimator for Weighted Squared-Error Loss
251(1)
References
252(1)
Exercises
253(4)
Reliability Block Diagramming in Complex Systems
257(52)
Introduction and Motivation
258(1)
Simple Illustrative Example
259(1)
Compression Algorithm and Various Applications
260(5)
Hybrid Tool to Compute Reliability for Complex Systems
265(3)
More Supporting Examples for the Hybrid Form
268(1)
New Polish Decoding (Decompression) Algorithm
268(3)
Overlap Technique
271(4)
Overlap Ingress--Egress Reliability Method
271(3)
Overlap Ingress--Egress Reliability Algorithm
274(1)
Multistate System Reliability Evaluation
275(6)
Simple Series System
276(1)
Active Parallel System
277(1)
Simple Parallel--Series System
278(1)
Simple Parallel System
279(1)
Combined System
279(2)
Discussion and Conclusions
281(28)
Appendix 6A: Overlap Algorithm Described
282(3)
Appendix 6B: Overlap Ingress--Egress Reliability Algorithm Applied, Example 1
285(13)
Appendix 6C: Overlap Ingress--Egress Reliability Algorithm Applied, Example 2
298(5)
References
303(3)
Exercises
306(3)
Index 309


Mehmet Sahinoglu, PhD, is Chair-Professor of the Computer Science Department at Troy University in Montgomery, Alabama. After teaching twenty years at his alma mater (BSEE) Middle East Technical University in Ankara, Turkey, he served as the founding dean and department chair in the College of Arts and Sciences at Dokuz Eylul University in Izmir, Turkey. More recently, Dr. Sahinoglu taught at Purdue University, Indiana, and Case Western Reserve University, Ohio, before joining Troy University as the university's first Eminent Scholar in Computer Science.
Permanent link: https://www.kriso.ee/db/9780470127872_pe.html
Keywords: