Noise is the enemy of detection and response. After data breaches, forensic investigators have often found warning signs left behind by adversaries, but these signs were buried amongst thousands of other security alerts, including countless false positives.
Machine learning and behavioral analytics can provide security teams the edge they need to reduce noise and accurately pinpoint attacks. Machine learning models can classify devices and compare current behavior to past behavior and peer behavior to isolate real attacks. Unlike static rules, machine learning models can dynamically adjust to ignore unusual but benign activity, drastically reducing false positives.
Attend this informative session to understand:
- Real-world examples of machine learning models used to detect attacks
- The key shortcomings with today’s approaches to machine learning and how security best practices and the right tools can overcome them