Conference Description
Key Takeaways
- Annual conference focused on applying machine learning techniques to information security challenges
- Brings together researchers and practitioners from industry, academia, and government sectors
- Covers adversarial thinking, Red and Blue team strategies, and ML-based defense approaches
- In-person event in Arlington, VA featuring keynotes, breakout sessions, and poster presentations
- Supports academic participation through published proceedings and student scholarships
About CAMLIS
The Conference on Applied Machine Learning in Information Security (CAMLIS) is an annual gathering that connects cybersecurity professionals with machine learning researchers to explore how ML techniques can address real-world security challenges. The 2026 edition takes place in Arlington, VA as an in-person event.
CAMLIS distinguishes itself through its cross-sector approach, drawing attendees from technology companies, academic institutions, government agencies, and security-focused organizations. The conference format emphasizes open dialogue over formal presentations, with programming designed to facilitate collaboration between researchers developing new ML approaches and practitioners implementing security solutions.
Machine Learning and Adversarial Security
The technical program addresses the application of machine learning across both offensive and defensive security domains. Sessions cover adversarial thinking, examining how attackers leverage ML and how defenders can anticipate these techniques. Red and Blue team strategies feature prominently, exploring the dynamics between attack simulation and defense implementation.
Content spans technical research and practical implementations, addressing the gap between academic ML advances and their deployment in production security environments. Attendees can expect discussions on emerging research directions alongside case studies of ML-based defense strategies in operation.
Event Format
CAMLIS 2026 features keynote sessions, expert-led breakout discussions, and poster sessions. The conference incorporates networking events and social gatherings throughout the program. This structure supports both formal knowledge transfer and informal exchanges between participants working on related problems.
The conference publishes proceedings and offers student scholarships, supporting academic participation and providing recognition for research contributions.
Who Should Attend
CAMLIS serves cybersecurity professionals, ML researchers, data scientists, and academic faculty and students working at the intersection of machine learning and security. The conference attracts security researchers, ML engineers, security analysts, and CISOs, along with professors and graduate students conducting related research. Attendees range from early-career professionals to senior practitioners and executives, with representation across technology, academia, and government sectors.