Virtualized Network Policy Verification for Anomaly Detection

The patent applies preliminary action by implementing a policy verification function that analyzes and validates policies before they are deployed to the network. The ML model performs anomaly detection on policy configurations in advance, identifying potential issues such as improper network behavior, service degradation risks, and unauthorized data exposure vulnerabilities before they can affect the actual network operation. This pre-validation approach allows automated policy generation to proceed quickly while maintaining reliability through prior security checks.

The patent replaces traditional mechanical or rule-based policy validation methods with a machine learning-based anomaly detection system. Instead of relying on simple checklist validations or manual reviews, the system employs ML models trained to recognize patterns of anomalous behavior in policies. This substitution significantly improves measurement precision in detecting subtle anomalies and ensuring policy authenticity, while the modular integration keeps the added complexity manageable through standardized interfaces and incremental deployment.

The patent applies partial action by implementing a two-stage validation process: first, a lightweight filtering mechanism performs rapid preliminary assessment of policies to identify obvious anomalies, and second, the full ML model is applied only to policies that require deeper analysis. This approach maintains high anomaly detection capability for complex cases while reducing average processing time by avoiding full ML analysis on all policies. The system performs just enough validation effort for each policy based on its risk profile.