DYnamic and Novel Advances in Machine Learning and Intelligent Cyber Security (DYNAMICS)
Event submitted on Tuesday, September 22nd 2020, approved by Charles Villanueva ✓
46 Days Until The Event
December 7th, 2020
United States
»
South, USA
»
Texas
» Austin
Event Website
This event has been tagged as follows:
Our Review
Every year we see more and more events being added to our directory that is combining Machine Learning and Cybersecurity; and this one is a perfect example. If you’re associated with either of these industries then we encourage you to get involved!
- Review written by Henry Dalziel on Tuesday, September 22nd 2020.
- If you would like to edit or ammend facts in my review please either send us a message or connect with me via LinkedIn.
Event Summary
The following description was either submitted by the Conference Organizer on Tuesday, September 22nd 2020, or created by us.
The 2020 DYnamic and Novel Advances in Machine Learning and Intelligent Cyber Security (DYNAMICS) Workshop will be held on Monday, December 7th.
The workshop will be co-located with the 2020 Annual Computer Security Applications Conference (ACSAC), held at the AT&T Hotel and Conference Center in Austin, Texas. Due to the evolving COVID situation, DYNAMICS will be held virtually.
Machine learning has become critical to the evolution and sustainability of cybersecurity. While the theoretical objectives and principles behind cybersecurity are still valid, traditional technologies that require humans in the loop to read log files, triage alerts, and harden devices are neither fast enough nor scalable enough to meet the demands of modern networks and attacks. While network traffic and devices have grown by orders of magnitude, the ability for operators to triage the resulting alerts has not.
The sophistication of threats has also increased substantially. Sophisticated zero-day attacks go undetected for months at a time. Attacks take place over extended periods, effectively outwaiting traditional intrusion detection technology. 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 learningbased offensive techniques.
This 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.
