This book constitutes the refereed proceedings of the 9th International Conference on Applications and Techniques in Information Security, ATIS 2018, held in Nanning, China, in November 2018. The 19 full papers were carefully reviewed and selected from 59 submissions. The papers are organized in the following topical sections: information security, information abuse prevention, security implementations, knowledge discovery, and applications.

This book constitutes the refereed proceedings of the 24th China Conference on Information Retrieval, CCIR 2018, held in Guilin, China, in September 2018. The 22 full papers presented were carefully reviewed and selected from 52 submissions. The papers are organized in topical sections: Information retrieval, collaborative and social computing, natural language processing.

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."

The application of formal methods to security protocol analysis has attracted increasing attention in the past two decades, and recently has been sh- ing signs of new maturity and consolidation. The development of these formal methodsismotivatedbythehostilenatureofsomeaspectsofthenetworkand the persistent e?orts of intruders, and has been widely discussed among - searchers in this ?eld. Contributions to the investigation of novel and e?cient ideas and techniques have been made through some important conferences and journals, such asESORICS, CSFW andACM Transactions in Computer Systems. Thus, formal methods have played an important role in a variety of applications such as discrete system analysis for cryptographic protocols, - lief logics and state exploration tools. A complicated security protocol can be abstractedasamanipulationofsymbolsandstructurescomposedbysymbols. The analysis of e-commerce (electronic commerce) protocols is a particular case of such symbol systems. There have been considerable e?orts in developing a number of tools for ensuring the security of protocols, both specialized and general-purpose, such as belief logic and process algebras. The application of formal methods starts with the analysis of key-distribution protocols for communication between two principals at an early stage. With the performance of transactions - coming more and more dependent on computer networks, and cryptography becoming more widely deployed, the type of application becomes more varied and complicated. The emerging complex network-based transactions such as ?nancial transactionsand secure groupcommunication have not only brought innovationstothecurrentbusinesspractice, butthey alsoposeabigchallenge to protect the information transmitted over the open network from malicious attack

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.