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Trustworthy Computing: Analytical and Quantitative Engineering Evaluation (Repost)

Posted By: DZ123
Trustworthy Computing: Analytical and Quantitative Engineering Evaluation (Repost)

M. Sahinoglu, "Trustworthy Computing: Analytical and Quantitative Engineering Evaluation"
English | 2007 | ISBN: 0470085126 | PDF | pages: 340 | 3.8 mb

"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.