Camlis

The deluge of information security data invites data-driven strategies for situational awareness, threat detection and remediation. Statistics and machine learning approaches to assess and automate elements of information security have become increasingly popular. However, there exist few venues for collegial information exchange in the level of technical detail appropriate for data science practitioners. The Conference on Applied Machine Learning for Information Security (CAMLIS) provides a venue for discussing applied work from researchers in academia, government research labs, national laboratories and FFRDCs, and information security data scientists in the industry.