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Cybersecurity is a data-rich and natural setting for machine learning (ML). However, lack of explainability is a major challenge. This session will propose cyber-informed ML, a paradigm emphasizing two directions of explainability, human-to-model and model-to-human. Will share practical examples of overcoming this challenge and discuss research needed for ML at the cybersecurity operations level.
Participants
Jeffrey Mellon
Speaker
Machine Learning Research Scientist, Carnegie Mellon University Software Engineering Institute
Clarence Worrell
Speaker
Senior Data Scientist, Carnegie Mellon University Software Engineering Institute
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