Distributed Anomaly Detection in Hierarchical Network Bridges

The patent segments the centralized anomaly detection system into distributed components at each bridge. Each bridge independently performs anomaly detection for its connected users using locally determined behavioral profiles, eliminating the need to transfer all user data to a central node. This segmentation reduces data traffic while maintaining detection capability at the edge of the network.

The patent transitions from a vertical centralized architecture to a horizontal distributed architecture where anomaly detection capabilities are distributed across multiple bridges. Each bridge operates in its own dimension/scope, determining profiles and detecting anomalies for its local users independently, thereby reducing the dimensional concentration of data traffic at a single central node.

The system segments anomaly detection functionality across multiple independent bridges, allowing each bridge to serve its local users autonomously. This segmentation enables the network to scale horizontally by simply adding more bridges without requiring a proportionally larger centralized processing node, thus maintaining scalability while preserving detection accuracy.

Each bridge performs self-service anomaly detection for its connected users by determining behavioral profiles locally and detecting anomalies independently. This self-service capability eliminates the need for centralized processing, allowing the network to scale freely by adding more self-sufficient bridges without increasing central node complexity or data traffic requirements.

The patent segments anomaly detection to occur at the local bridge level rather than aggregating all data centrally. Each bridge maintains sensitivity to localized anomalies in its own user group by determining and applying behavioral profiles locally, preventing information loss that would occur through central aggregation and normalization of diverse user behaviors.

The system applies local quality by allowing each bridge to determine behavioral profiles and detect anomalies specific to its local user group. This localized approach preserves the unique characteristics and anomaly patterns of each bridge's user base, preventing the loss of localized information that occurs when all data is aggregated and processed uniformly at a central node.

The patent segments user data processing to occur locally at each bridge rather than centralizing raw user data collection. Each bridge determines behavioral profiles and detects anomalies for its own users without transferring detailed user information to a central node, thereby segmenting privacy risk containment to local levels while maintaining detection accuracy.

The patent introduces behavioral profiles as an intermediary representation that mediates between raw user data and anomaly detection. Instead of transferring or storing sensitive raw user data centrally, the system uses aggregated behavioral profiles as intermediaries that capture essential patterns for detection while preserving user privacy, thus reducing privacy risks associated with centralized data collection.