MaL2CSec 2019 - Workshop on Machine Learning for Cybersecurity (MaL2CSec)

This workshop aims at providing a forum for people from academia and industry to communicate their latest results on theoretical advances, industrial case studies, that combines machine learning techniques such as reinforcement learning, adversarial machine learning, and deep learning with significant problems in cybersecurity. Research papers can be focused on offensive and defensive applications of machine learning to security. Potential topics include, but are not limited to:
- Adversarial training and defensive distillation
- Attacks against machine learning
- Black-box attacks against machine learning
- Challenges of machine learning for cyber security
- Ethics of machine learning for cyber security applications
- Generative adversarial models
- Graph representation learning
- Machine learning forensics
- Machine learning threat intelligence
- Malware detection
- Neural graph learning
- One-shot learning; continuous learning
- Scalable machine learning for cyber security
- Steganography and steganalysis based on machine learning techniques
- Strength and shortcomings of machine learning for cyber-security