Conference on Applied Machine Learning for Information Security (CAMLIS) 2021
Event submitted on Sunday, September 12th 2021, approved by Content Team ✓
This event has been tagged as follows:
In addition to presentations, posters, and tutorials, the conference will have a single machine learning track. A 20-minute presentation will be interspersed with a lengthy (up to 10-minute) discussion time following each presentation. The organizers are also encouraging tutorial suggestions on machine learning techniques or infosec issues this year. The 60-minute tutorials should focus on areas of applied research or practice that are well-established. The CAMLIS community will give preference to instructional subjects with broad application and/or interest. Speaking and instructional sessions will be videotaped for public use after the event. Highly recommended for those working with Machine Learning technologies.
Conference Event Summary
The following description was either submitted by the Conference Organizer on Sunday, September 12th 2021, or created by us.
The Conference on Applied Machine Learning in Information Security (CAMLIS) is a venue that gathers researchers and practitioners to discuss applied and fundamental research on machine learning in information security.
The conference is single-track and will include presentations, posters, and tutorials. Presentations will be 20 minutes with a lengthy (up to 10 minutes) opportunity for a discussion period after each talk. This year, organizers also encourage proposals for tutorials on either machine learning techniques or infosec problems. 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.
Participation from academics working in information security, government research labs, national laboratories, and FFRDCs, and information security data scientists in the industry are encouraged