Too often, security posture is reactive: that is, we try to remedy an issue after it has occurred. We patch vulnerable systems, add firewalls once we have detected intrusions, etc. In this course, we will focus on how to predict impending security incidents, so that we can proactively select the right responses.
Less customizable than average. This course is primarily for participants with at least a basic technical background.
In-person or remote
Remote, in-person, and pre-recorded sections, hybrid.
This course is primarily appropriate for professionals working in technology (dev ops, CS).
To acquire an understanding of some of the tools needed for building predictive engines against cybersecurity threats.
- Objectives, assumptions, requirements
- Tools: decision trees, neural networks
- Web server vulnerabilities including discussion of design choices and possible alternatives
- Predictive analytics for user behavior
- Including discussion of design choices and possible alternatives
- What are good applications for this kind of technique?
- We'll run, on the board, through a couple of scenarios, and debate whether these are valid applications or not; if so, which tools would they use, which metrics would they rely on, etc.
Background in CS or IT is strongly preferred. Some amount of mathematical background is helpful as well.
Copies of presentations and relevant papers will be provided to participants.
To learn about our custom programs and any upcoming open enrollments, reach out to Michael Lisanti.