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Ijitis 125 by kiruajkk is a document available to read on EtoBox.

This research paper addresses the challenge of detecting insider threats using machine learning (ML) within Security Information and Event Management (SIEM) systems, specifically focusing on the top 10 critical use cases for internal user behavior anomalies. By integrating the Random Cut Forest algorithm into the Wazuh/OpenSearch platform, the study demonstrates a significant reduction in false positives and improved detection rates for high-risk scenarios. The findings provide a replicable framework for or

Author

kiruajkk

Language

EN