Featured Application: The systematic and methodological process of analysis described in this document will provide a complete understanding of the life cycle of a malware specimen in terms of its behavior, operation, interaction with the environment, methods of concealment and obfuscation, system updates, and communications.Abstract: Malware threats pose new challenges to analytic and reverse engineering tasks. It is needed for a systematic approach to that analysis, in an attempt to fully uncover their underlying attack vectors and techniques and find commonalities between them. In this paper, a method of malware analysis is described, together with a report of its application to the case of Flame and Red October. The method has also been used by different analysts to analyze other malware threats like 'Stuxnet', 'Dark Comet', 'Poison Ivy', 'Locky', 'Careto', and 'Sofacy Carberp'. The method presented in this work is a systematic and methodological process of analysis, whose main objective is the acquisition of knowledge as well as to gain a full understanding of a particular malware. Using the proposed method to analyze two well-known malware as 'Flame' and 'Red October' will help to understand the added value of the method.
Abstract. Fuzzy relational databases have been extensively studied in recent years, resulting in several models and constructs, some of which are implemented as software layers on top of diverse existing database systems. Fuzzy extensions to query languages and end-user query interfaces have also been developed, but the design of programming interfaces has not been properly addressed. In this paper, we describe a software framework called fJdbc that extends the Java Database Connectivity API by allowing fuzzy queries on existing relational databases, using externally-stored metadata. Since the main design objective of this extension is usability for existing database programmers, only a restricted subset of extensions (supported also by an extended object modelling notation) has been included. The overall design of the framework and some experimental data are also described.
Rule-based adaptive hypermedia systems personalize the structure of the hypermedia space using an inference mechanism that operates on a specific knowledge representation about its users. Approximate quantifiers are very frequently used in human language expressions that entail the summarization of a large number of facts. We describe how quantified expressions can be used in adaptation rules to specify common adaptation behaviors, enhancing rule's expressive power for the human expert. Those quantified expressions can be implemented through fuzzy quantification mechanisms operating on fuzzy linguistic labels and relations, and can be integrated as extensions in generalpurpose rule-based adaptive hypermedia systems.
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