Welcome to CyberPatterns 2014

A pattern represents a discernible regularity in the world or in manmade designs. In the prescriptive view, a pattern is a template from which instances can be created; while in the descriptive view, the elements of a pattern that repeat in a predictable manner can be observed and recognised. Similar to theories in sciences, patterns explain and predict regularities in a subject domain. In a complicated subject domain like cyberspace, there are usually a large number of patterns that each describes and predicts a subset of recurring phenomena, yet these patterns can interact and interfere with each other and be coordinated and composed together. The pattern-oriented research method studies a subject domain by identifying the patterns, classifying and categorising them, organising them into pattern languages, scoping their boundaries, investigating the interactions between them, devising mechanisms and operations for detecting and predicting their occurrences, and facilitating their instantiation or simulation.

Topics

The topics cover all aspects of cyberpatterns, including, but not limited to, the following:

  • Scientific foundation of pattern-oriented research methods for systematic analysis of big data in order to discover the reusable knowledge.
  • Engineering practice in the development of platforms, algorithms and tools for analysis of big data for pattern discovery and applications.
  • Construction of infrastructure for a sharable knowledge-base of cyberpatterns, e.g. in aiding system design for practitioners and teaching students, with possible tool support to guide usage by developers.
  • Experiments and case studies in developing and using cyberpatterns, as well as experience reports.
  • Identification of research problems and understanding issues that hamper wider adoption of cyberpatterns and suggesting remediation measures.
  • Future vision of the use of cyberpatterns in novel cyber domains, such as the cloud or cyberphysical systems, and innovative uses of design patterns such as in pattern recognition.

Some indicative topics include, but are not restricted to:

  • Security, attack, advanced cyber threat and forensic patterns
  • Design patterns, dependable and trustworthy patterns
  • Enterprise and architectural patterns
  • User behaviour, system usage, network traffic patterns
  • Patterns in social network, cyberphysical and cloud systems
  • Big data, data mining, machine learning, statistical data analysis
  • Pattern visualization, simulation, anomaly detection

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