The present volume arose from the need for a comprehensive coverage of the state of the art in security protocol analysis. It aims to serve as an overall course-aid and to provide self-study material for researchers and students in formal methods theory and applications in e-commerce, data analysis and data mining. The volume will also be useful to anyone interested in secure e-commerce. The book is organized in eight chapters covering the main approaches and tools in formal methods for security protocol analysis. It starts with an introductory chapter presenting the fundamentals and background knowledge with respect to formal methods and security protocol analysis. Chapter 2 provides an overview of related work in this area, including basic concepts and terminology. Chapters 3 and 4 show a logical framework and a model checker for analyzing secure transaction protocols. Chapter 5 explains how to deal with uncertainty issues in secure messages, including inconsistent messages and conflicting beliefs in messages. Chapter 6 integrates data mining with security protocol analysis, and Chapter 7 develops a new technique for detecting collusion attack in security protocols. Chapter 8 gives a summary of the chapters and presents a brief discussion of some emerging issues in the field.
Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention. The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.
Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining.
Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining.
Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention. The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.
The present volume arose from the need for a comprehensive coverage of the state of the art in security protocol analysis. It aims to serve as an overall course-aid and to provide self-study material for researchers and students in formal methods theory and applications in e-commerce, data analysis and data mining. The volume will also be useful to anyone interested in secure e-commerce. The book is organized in eight chapters covering the main approaches and tools in formal methods for security protocol analysis. It starts with an introductory chapter presenting the fundamentals and background knowledge with respect to formal methods and security protocol analysis. Chapter 2 provides an overview of related work in this area, including basic concepts and terminology. Chapters 3 and 4 show a logical framework and a model checker for analyzing secure transaction protocols. Chapter 5 explains how to deal with uncertainty issues in secure messages, including inconsistent messages and conflicting beliefs in messages. Chapter 6 integrates data mining with security protocol analysis, and Chapter 7 develops a new technique for detecting collusion attack in security protocols. Chapter 8 gives a summary of the chapters and presents a brief discussion of some emerging issues in the field.
This book constitutes the refereed proceedings of the 18th Australian Joint Conference on Artificial Intelligence, AI 2005, held in Sydney, Australia in December 2005. The 77 revised full papers and 119 revised short papers presented together with the abstracts of 3 keynote speeches were carefully reviewed and selected from 535 submissions. The papers are catgorized in three broad sections, namely: AI foundations and technologies, computational intelligence, and AI in specialized domains. Particular topics addressed by the papers are logic and reasoning, machine learning, game theory, robotic technology, data mining, neural networks, fuzzy theory and algorithms, evolutionary computing, Web intelligence, decision making, pattern recognition, agent technology, and AI applications.
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