"Explainability in Cybersecurity Data Science"
"Explainability in Cybersecurity Data Science"
Cybersecurity is data-rich, making it an ideal setting for Machine Learning (ML), but many challenges impede ML deployment in cybersecurity systems and organizations. According to researchers from Carnegie Mellon University's Software Engineering Institute (SEI), one significant challenge is that the human-machine relationship is rooted in a lack of explainability. Cybersecurity data science has two directions of explainability: model-to-human and human-to-model.