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.

We invite paper and/or extended abstract submissions on the direct application or adaptation of statistics, machine learning, deep learning and data science to infosec relevant areas including:

-Insider threat detection
-Network and endpoint forensics
-Governance, compliance and exfiltration detection
-Detection of script-based and malware-less attacks
-Automated malware detection and classification
-Vulnerability assessment
-Open source software security
-Dark web analytics
-Cyber threat intelligence applications
-Cybersecurity risk management
-Machine learning models as attack surface
-Political disinformation and computational propaganda

We encourage submissions that include analytic or predictive themes:

-Statistical analysis on large and small datasets
-Unique considerations of base-rate fallacy for data science in information security
-Infosec data sources and exploratory data analysis
-Unique approaches to dataset visualization
-Adversarial machine learning in the context of infosec
-Original or cross-domain deep learning architectures applied to information security data
-Natural language processing, image analysis, signal analysis
-Reinforcement learning for automating security tasks
-Unsupervised and semi-supervised approaches
-Explainable ML for Infosec
-Multi-view, multi-task, and multi-source learning
-Deep Bayesian Learning
-Knowledge distillation and transfer learning paradigms
-Graph embedding (node, edge, graph-level)

We invite both original submissions and presentations submitted very recently at other venues (since January 2021). New this year is the inclusion of accepted works into proceedings that will be indexable by major academic search engines (e.g., Google Scholar).

Submitted papers should be formatted in a single column format in 12 point format, single spaced. While there is no “hard” limit to the length of the papers, we encourage authors not to exceed 12 pages (preferred length 6-10 pages). The presentation of the content should be concise and precise to help reviewers judge papers on their methodological novelty and contributions to a particular application of security. Each paper will undergo a double blind review, and will be considered for a publication in the proceedings and as a talk or as a poster. A full paper is not required. Authors are also welcome to submit a two page extended abstract that would be considered for inclusion in the program as a talk or as a poster (the latter option is in the same manner as prior years of CAMLIS).

The data, representations, model settings, evaluation set up, and performance metrics should all be clearly described in the submissions. While not required, authors are strongly encouraged to provide the code, documentation, and data from their research in a publicly accessible format or repository (e.g., GitHub). This will help to facilitate scientific reproducibility of impactful research within our community and help to generate innovative research in a more timely and streamlined manner.

Accepted talks will be presented as talks of 20 minutes in length with up to 10 minutes of discussion period after each talk. Talks will also be recorded and made publicly available after the conference. The poster session will be held live and in-person during the CAMLIS event.

We encourage participation from academics working in information security, government research labs, national laboratories and FFRDCs, and information security data scientists in industry.

Important dates are:

Wednesday, September 1, 2021: Submission deadline
Friday, October 1, 2021: Speakers notified
Thu/Fri, November 4-5, 2021: Conference
Important websites are:

If authors are unable to attend due to COVID travel restrictions or other issues, we will work with them to provide an alternative means of presentation.

Conference website: https://www.camlis.org/
Submission link: https://easychair.org/conferences/?conf=camlis2021
Venue: Sands Capital Management, 1000 Wilson Boulevard, 30th Floor, Arlington, VA 22209