Conference on Applied Machine Learning in Information Security (CAMLIS)
InfoSec Conference Summary
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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.
You are invited to submit title and abstract submissions on the direct application of statistics, machine learning, deep learning and data science to information security.
The event is single-track and will include presentations, posters and tutorials. Presentations will be 20 minutes with a lengthy (up to 10 minutes) opportunity for discussion period after each talk. This year, proposals for tutorials on either machine learning techniques or infosec problems are encouraged. Tutorials, which are targeted for 60 minutes, should be on mature areas of applied research or practice. Preference will be given to tutorial topics having broad applicability to and/or garnering wide interest by the CAMLIS community. All talks and tutorials will be recorded and made publicly available after the conference.
The organizers also encourage participation from students and academics working in information security, government research labs, national laboratories and FFRDCs, and information security data scientists in industry. A small number of student travel grants will be awarded for students requiring support, with preference given to student presenters. A separate announcement for student travel grants will be released in mid-August.