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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.
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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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