SAIS 2018 - Security Analysis of Information Systems Track

In a meshed world, organizations collect hundreds of Gigabytes of data from the events occurring in the information systems and business operations. However, just collecting those data is not meaningful without a further analysis. Post-mortem analysis of security attacks have shown that much of the required information to detect and prevent abnormal behaviors is hidden in logs.
Knowledge management allows to use a broad range of techniques to meet the security analysis goals of an organization by giving meaning to the collected data and creating new added-value knowledge. Traditionally, artificial intelligence techniques have been used to detect patterns in high dimensional data. Moreover, future trends in knowledge management lead to an “intelligent” representation of the human knowledge to create dynamic, able to learn, and predictive security models.
The purpose of this track is to create a space to discuss about the analysis of the security, and the way how the knowledge management and the artificial intelligence contribute to this analysis.
The topics of this track include, but are not limited to:
Approaches for security analysis (formal, semi-formal, by design)
Process modeling applied to the analysis of security
Models for security analysis
Elicitation of business rules for security analysis
Knowledge extraction for security analysis (patterns, abnormal security behavior, security event correlation)
Log management (anonymization, collection of data log, storage, auditing, data visualization)
Context-aware security analysis
Techniques for security analysis (data mining, neural networks, clustering, machine learning)