Neural Network Signatures for Session Interoperability
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Solution Overview
Problem
Unified Communications systems face complexity in signaling interoperability, leading to issues with device integration, security, and threat detection, particularly due to the vast number of devices and signaling permutations, which existing technologies struggle to manage efficiently and effectively.
Innovation Solution
The use of neural networks, specifically autoencoder neural networks, to compute and utilize signatures for identifying and categorizing communications sessions, devices, and users, enabling automated detection of anomalies and modification of signaling to ensure interoperability and security, while also providing identity verification and threat detection services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional signaling management methods are used to handle diverse communications sessions, then device integration and interoperability can be achieved, but system complexity increases and threat detection efficiency deteriorates
Solution Approach 1:
The patent transforms the complex signaling data into simplified signatures by extracting and comparing key parameters. Instead of managing full signaling permutations, the system identifies and compares essential signature elements, reducing complexity while maintaining adaptability for diverse device integration
Solution Approach 2:
The patent creates simplified copies (signatures) of communications session characteristics instead of managing the complete original signaling data. These signature copies enable efficient comparison and threat detection without requiring full signaling interoperability management, thus reducing system complexity
2Reliability
If comprehensive signaling analysis is performed to detect threats, then security improves, but processing time increases
Solution Approach 1:
The patent extracts only the essential signature elements from complete signaling data for analysis. By taking out and comparing only the critical signature parameters rather than analyzing entire signaling permutations, the system maintains high security detection capability while significantly reducing processing time
Solution Approach 2:
The patent performs partial analysis by focusing on signature comparison rather than complete signaling analysis. This partial action approach provides sufficient security detection for threat identification without the excessive processing time required for comprehensive signaling permutation analysis
3Adaptability or versatility
If manual device configuration and integration is performed, then interoperability can be achieved, but operational efficiency deteriorates
Solution Approach 1:
The patent enables automated device integration through signature-based identification and matching. The system performs self-service by automatically comparing device signatures against known signatures, eliminating the need for manual configuration while maintaining interoperability across diverse communications devices
Data Source
AI summary
The present invention relates to systems, apparatus and methods for the computation and use of session, device and/or user signatures for determining communications session types, device types, and/or user signatures. An exemplary method in accordance with an embodiment of the invention includes: receiving a first set of session control messages belonging to a first communications session, the first set of session control messages including at least one session control message; extracting a first set of features from the first set of session control messages; operating one or more neural networks to identify a group session signature to which the first set of session control messages corresponds based on the first set of features, the identified group session signature being one of a plurality of group session signatures.