Browsing by Author "Adams, Emily K."

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    2020 CACR AI/ML Lessons Learned Report
    (2020-07-31) Kiser, Ryan; Adams, Emily K.; Cushenberry, Austin; Abhinit, Ishan; Shute, Kelli
    Since Fall of 2019, the Indiana University Center for Applied Cybersecurity Research (CACR) has been exploring the application of machine learning to cybersecurity workflows with the intent of developing the applicable expertise necessary to maintain a commanding lead in the cybersecurity domain where machine learning solutions are expected to increasingly become the norm. In order to serve the objectives laid out in the project charter, CACR primarily worked in partnership with OmniSOC and researchers at Rochester Institute of Technology to explore the application of the ASSERT research prototype to SOC analyst workflows. The intent of this effort was to better understand both the utility of the ASSERT prototype and the challenges associated with the implementation of machine learning approaches to cybersecurity workflows more broadly.
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    Identifying Malicious Threats to Scientific Data Integrity Using MITRE ATT&CK®
    (2022-08) Adams, Emily K.
    In this paper malicious tactics and techniques leveraged to explicitly manipulate the integrity of transient workflow data, data products, or derived metadata within scientific workflows are considered. This document leverages the MITRE ATT&CK Enterprise knowledge base of adversary tactics and techniques, based on real-world observations, as the foundation for a scoped analysis enumerating malicious attacks against data integrity within scientific workflows.