Preventing Threats using Machine Learning, Contextualization and Predictability

Logo
Presented by

David Dufour, Senior Director of Security Architecture, Webroot

About this talk

With the rapidly accelerating nature of attacks on network infrastructure and software systems approaches such as static block lists, manual policy configurations and other current prevention techniques have become outdated. Through the use of distributed computing, contextualization and machine learning it is possible to build tools that analyze data across multiple threat vectors allowing for the development of predictive algorithms and a greater understanding of an organizations threat landscape. We will walk through common machine learning techniques, discuss contextualization, how predictive logic works and see a demonstration of contextualized threat intelligence.

Related topics:

More from this channel

Upcoming talks (0)
On-demand talks (206)
Subscribers (62369)
OpenText Cybersecurity provides comprehensive security solutions for companies and partners of all sizes. From prevention to detection and response, to recovery, investigation and compliance, our unified end-to-end platform helps customers build cyber resilience via a holistic security portfolio. Powered by actionable insights from our real-time contextual threat intelligence, OpenText Cybersecurity customers benefit from high efficacy products, a compliant experience, and simplified security to help manage business risk. Discover cyber resilience at carbonite.com and webroot.com.