The deluge of information security data invites data-driven strategies for information security situational awareness, threat detection and remediation. Statistics and machine learning approaches applied to assess and automate elements of information security have become increasingly popular in academia, government, national labs, and in industry, but with few venues for collegial information exchange in detail appropriate for data science practitioners in the information security space.
The Conference on Applied Machine Learning in Information Security (CAMLIS) is a venue for discussing applied research on machine learning, deep learning and data science in information security.