AI and Intelligent Systems: Engineering, Medicine & Society(AIISEMS)

Hewlett High School, Hewlett, NY, USA

Publications

• Research Article   
  Evaluating ML Performance in EDR and XDR Systems Against Common Cyber Threats
  Author(s): Ryan Ginsburg*, Matthew Liu, Felipe Marin and Rio Williams
  
  The rising sophistication of modern cyberattacks creates a growing demand for companies to be up-to-date in their online and network security. This study aims to compare the Endpoint Detection and Response (EDR) and Extended Detection and Response (XDR) systems empowered by machine learning. Using 58 days of labeled telemetry from the Los Alamos National Laboratory (LANL) Comprehensive Multi-Source Cyber-Security Events dataset to build two parallel datasets: an EDR view comprising authentication and process logs, and an XDR view that expands on the EDR telemetry with DNS queries and network flow records. Both datasets were segmented into 10-second windows, engineered into statistical features, and chronologically split into training and testing partitions based on red team activity. We evaluated logistic regression, ensemble tree methods (Random Forest with SMOTE, Balanced Random For.. Read More»
  

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