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Download the Working Paper "Mitigating Machine Learning Risks within a Vulnerable SIEM to Prevent Biased SOC Decisions"

  • May 31, 2033
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Working Paper: Mitigating Machine Learning Risks within a Vulnerable SIEM to Prevent Biased SOC Decisions

In this working paper, authors Landmesser and Vommi explore weaknesses in machine learning systems used by a SIEM that present a technical issue, which can also negatively influence decisions made by SOC personnel. Incorrect ML classifications from APT attacks result in incorrect security decisions based on SIEM output, causing an even more damaging impact on required incident response.

This material is based on work supported by the National Science Foundation (NSF) under Grant Number DUE-1204533DUE-1601150, and DUE-2054753Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of NSF.

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