DYnamic and Novel Advances in Machine Learning and Intelligent Cyber Security (DYNAMICS) Workshop

December 3, 2018 - December 7, 2018

San Juan, Puerto Rico

12/03/2018 09:00 12/07/2018 17:00 America/New_York Cybersecurity Conference: DYnamic and Novel Advances in Machine Learning and Intelligent Cyber Security (DYNAMICS) Workshop For more info, please visit: https://infosec-conferences.com/events-in-2018/dynamics/ San Juan, Puerto Rico


Conference Description (submitted by organizer)

The 2018 DYnamic and Novel Advances in Machine Learning and Intelligent Cyber Security (DYNAMICS) Workshop will be held on Monday, December 3rd and Tuesday, December 4th, 2018. The workshop will be co-located with the 2018 Annual Computer Security Applications Conference (ACSAC) at the Condado Plaza Hilton in San Juan, Puerto Rico, USA.

Machine learning has become critical to the evolution and sustainability of cyber security. While the theoretical objectives and principles behind cyber security are still valid, traditional technologies that require humans to read log files, triage alerts, and harden devices are neither sufficiently fast, nor scalable enough, to meet the demands of modern networks and attacks. While the volume of network data, and the number of devices on network, have grown by orders of magnitude, the rate at which humans can triage alerts has not.

The sophistication of threats has also increased substantially. Sophisticated zero-day attacks may go undetected for months at a time. Attack patterns may be engineered to take place over extended periods of time, making them very difficult for traditional intrusion detection technologies to detect. Worse, new attack tools and strategies can now be developed using adversarial machine learning techniques, requiring rapid co-evolution of defenses that match the speed and sophistication of machine learning-based offensive techniques.

This two-day workshop is intended to focus on novel applied and theoretical work that combines machine learning techniques such as reinforcement learning, adversarial machine learning, and deep learning with significant problems in cybersecurity. We consider both offensive and defensive applications of machine learning to security, with narrow topics grouped into six major topic areas presented over two days.