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 configuration, identity verification, 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.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional signaling management methods are used to handle diverse devices and call flows, then device compatibility and call completion can be achieved, but system complexity and configuration difficulty increase significantly
Solution Approach 1:
The patent creates virtual copies of signaling interactions by capturing and storing signaling patterns from successful call flows. These captured patterns serve as templates that can be replicated and applied to new devices and scenarios, eliminating the need to manually configure each device individually while maintaining compatibility
Solution Approach 2:
The system enables self-service by allowing new devices to automatically register their signaling patterns and receive automated configuration based on matched templates. The device itself provides information about its capabilities and behavior, and the system automatically generates appropriate signaling rules without requiring manual intervention from operators
2Reliability
If manual configuration and testing of each device is performed, then signaling interoperability can be ensured, but time consumption and operational efficiency decrease
Solution Approach 1:
The system performs preliminary action by pre-capturing and analyzing signaling patterns from successful call flows before they are needed. These pre-processed templates are stored and ready for immediate application when new devices or call flows need to be configured, eliminating the need for time-consuming manual testing and configuration
Solution Approach 2:
The system implements feedback by continuously monitoring call flows and device behavior, comparing actual signaling against expected patterns, and automatically updating templates based on observed successes and failures. This closed-loop feedback mechanism ensures interoperability is maintained while reducing manual intervention
3Reliability
If comprehensive security checks and identity verification are implemented, then network security and fraud prevention improve, but processing time and system overhead increase
Solution Approach 1:
The system performs preliminary security verification by analyzing signaling patterns during the call setup phase before full communication begins. Identity verification and security checks are embedded in the initial signaling exchange, allowing security validation to occur concurrently with normal call establishment rather than as a separate sequential step
Solution Approach 2:
The signaling pattern matching system serves multiple functions simultaneously: it enables device identification, performs security verification, detects fraud patterns, and facilitates call routing all through the same template-matching mechanism. This multi-functionality reduces overhead by eliminating the need for separate processing systems for each security and routing task
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 system in accordance with an embodiment of the invention includes: a first device, the first device including: a receiver that receives a first set of session control messages belonging to a first communications session, said first set of session control messages including at least one session control message; a feature extractor that extracts a first set of device features from the first set of session control messages; and a first neural network that determines a device signature from the first set of session control messages based on said set of device features.