Router Traffic Histograms for Encrypted Application Identification

The patent introduces router traffic data as an intermediary medium to identify applications. Instead of inspecting packets directly at the endpoint (which requires high computational resources and violates privacy), the system uses traffic characteristics collected by the router as a proxy to infer application identity. This intermediary approach enables application identification with significantly lower computational burden and without invading user privacy.

The patent replaces the traditional mechanical packet inspection approach with a data-driven classification system. Instead of performing deep packet inspection and decryption at the endpoint, the system uses machine learning models trained on router-collected traffic characteristics to classify applications. This substitution dramatically reduces computational requirements while maintaining identification accuracy.

The patent uses router traffic data as an intermediary that indirectly reveals application identity without exposing user privacy. The router collects network-level characteristics (such as packet sizes, inter-arrival times, protocol types) which are sufficient for application classification but do not expose the actual content, user behavior patterns, or device-specific information. This intermediary approach enables identification while preserving user privacy.

The patent segments the identification process into two independent parts: (1) traffic characteristic collection at the router level, and (2) application classification using machine learning. This segmentation allows the system to work with anonymized traffic data rather than raw packet contents, thereby maintaining privacy while achieving accurate application identification.

The patent leverages the router as an intermediary that already collects traffic data for network management purposes. By utilizing this existing data collection infrastructure, the system can scale to large numbers of devices without requiring additional endpoint resources. The router's centralized position in the network allows it to aggregate traffic characteristics from multiple devices efficiently, enabling scalable application identification across the entire network.

The patent makes the router perform multiple functions: it continues to handle normal network routing while simultaneously collecting traffic characteristics for application identification. This multi-functionality approach avoids the need for dedicated identification hardware at each endpoint, thereby improving scalability. The same router infrastructure serves both networking and security/identification purposes.

The patent uses router traffic data as an intermediary that captures application identification information without requiring packet decryption. The router collects network-level characteristics (packet sizes, timing patterns, protocol sequences) that are sufficient for accurate application classification. This approach eliminates the time-consuming decryption process while maintaining high identification accuracy, as the traffic characteristics themselves contain distinctive application fingerprints.

Instead of performing complete deep packet inspection and decryption (excessive action), the patent applies partial inspection by focusing only on network-level traffic characteristics that are sufficient for application identification. This partial action approach achieves the necessary identification accuracy without the computational overhead of full packet analysis, thereby reducing processing time significantly.