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Network Anomaly Detection

A global telecom firm struggling to quickly identify unauthorized network usage amid increasing cyberattacks across multiple subsidiaries.


  • Data centralization is a time-consuming and expensive process with subsidies in different data jurisdictions which use different cloud providers.
  • Centralized model training creates long lead times for deployment that mean updated models come too late to identify novel attacks.
  • Lagging time to analysis means that cyber criminals access networks for months before anomalies are detected - creating legal, reputational, and operational risks.


  • Devron could deploy Synergy to securely train models across datastores in multiple jurisdictions without exposing data through costly centralization.
  • With faster access to more training data, time to deployment decreases while Synergy identifies suspicious activity with over 95% accuracy.
  • Continuous model updates would also allow the proactive identification of unauthorized activity and novel attack vectors.
Network Anomaly Detection
Network Anomaly Detection
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