Network Threat Detection via Behavioral Clustering

The patent introduces behavioral clusters as intermediaries between individual network entities and the threat detection system. Instead of directly analyzing data from each entity, the system groups entities into clusters based on behavioral similarities, then performs threat detection at the cluster level. This intermediary layer simplifies information collection while maintaining detection accuracy, as the cluster behavior represents the collective state of multiple entities without requiring direct access to each individual entity.

The patent merges information from multiple network entities by grouping them into behavioral clusters. Instead of collecting and analyzing data from each entity separately (which would be time-consuming and unreliable when entities are offline or busy), the system combines their behaviors into cluster-level patterns. This merging approach maintains detection reliability by considering collective behavior while significantly reducing the time required for information collection, as the system only needs to aggregate data at the cluster level rather than individually from each entity.

The patent transitions from analyzing individual entity attributes to analyzing cluster-level behavioral patterns, effectively changing the dimension of analysis. Instead of examining each entity's raw data separately, the system creates a new dimensional space where entities are represented by their cluster memberships and aggregated behavioral features. This dimensional transformation maintains measurement precision by preserving the essential behavioral characteristics in the aggregated cluster representation, while simultaneously improving ease of operation by working with consolidated cluster data rather than individual entity data.