Hyprfire's government case studies provide compelling examples of network detection and response (NDR). Leveraging machine learning for NDR allows for advanced intelligence, which can be integrated with response-capable solutions such as NAC and SIEM. This enables organizations to have a clear visibility into their network traffic, a significant improvement over the traditional approach of using thresholds and pre-defined traffic patterns 4.

For instance, machine learning can detect lateral movements within a network, which would traditionally be flagged based on threshold values. This becomes particularly useful for chattier hosts such as file servers and vulnerability scanners, which naturally interact with more hosts. Machine learning can identify anomalous destinations and times of day for each host, ensuring no visibility or coverage gaps 4.

Similarly, the application of machine learning in detecting command and control (C2) communications has evolved NDR solutions. While traditional methods involve maintaining a list of known C2 nodes and triggering an alert when communication is detected, machine learning allows for immediate action to stop the communication 4.

In summary, Hyprfire's case studies demonstrate the efficacy of machine learning in enhancing NDR, enabling a more proactive and effective approach in managing network security.