Problems and mitigation strategies for developing and validating statistical cyber defenses

Michael Atighetchi, Michael Jay Mayhew, Rachel Greenstadt, Aaron Adler

    Research output: Contribution to journalArticlepeer-review

    Abstract

    The development and validation of advanced cyber security technology frequently relies on data capturing normal and suspicious activities at various system layers. However, getting access to meaningful data continues to be a major hurdle for innovation in statistical cyber defense research. This paper describes the data challenges encountered during development of the machine learning approach called Behavior-Based Access Control (BBAC), together with mitigation strategies that were instrumental in allowing R&D to proceed. The paper also discusses results from applying a spiral-based agile development process focused on continuous experimental validation of the resulting prototype capabilities.

    Original languageEnglish (US)
    Pages (from-to)25-29
    Number of pages5
    JournalCrossTalk
    Volume27
    Issue number2
    StatePublished - Mar 2014

    ASJC Scopus subject areas

    • Software
    • Human-Computer Interaction

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