Applying machine learning to network centric security
2019-10-10T12:30:00 2019-10-10T12:30:00 - 2019-10-10T13:00:00
The common denominator of all devices in the enterprise environment is the network traffic, making network-centric security the most effective approach in defending modern heterogeneous enterprise environments. Flow-data analysis leverages network traffic meta-data (source, destination, protocols, packet count, etc) and focuses on traffic patterns and subtle changes in network communication behaviour to detect advanced attacks and compromised endpoints.
We will explore how applying machine learning to network traffic enables network traffic security analytics solutions to improve detection of advanced threats that might target the entire range of network-connected devices.
- Why network centric security is the most effective approach in defending modern heterogeneous enterprise environment
- Content analysis vs network flow analysis
- Semi-supervised learning vs supervised and unsupervised machine learning
- Specialized machine learning algorithms to improve detection of advanced threats