SIP Data Processing for Lawful Interception Accuracy
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Solution Overview
Problem
The complex and error-prone manual process of lawful interception of SIP messages in communication systems, particularly in lawful interception scenarios, leads to misunderstandings between communication service providers and law enforcement agencies regarding the correctness of intercepted data formatting and session identification, which is time-consuming and costly.
Innovation Solution
A method employing a lawful interception system with a mediation and delivery function (MF2/DF2) and a SIP data processing function (DPF) that decodes, normalizes, and groups SIP messages to facilitate automatic identification of SIP-based traffic flows using machine learning and artificial intelligence tools, reducing manual errors and misunderstandings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual traffic identification is performed by communications service providers, then traffic flow identification can be done, but the process becomes long, expensive and error-prone
Solution Approach 1:
The patent replaces the manual mechanical process of traffic identification with an automated system using machine learning models. The system automatically identifies traffic flows by analyzing intercepted SIP messages through trained ML models, eliminating the need for manual inspection and correlation of messages by operators.
Solution Approach 2:
The patent introduces an intermediary automated processing system that acts as a mediator between message interception and traffic identification. This intermediary system includes components for message extraction, correlation, and ML-based classification, which bridges the gap between raw intercepted data and meaningful traffic flow identification.
2Measurement precision
If manual traffic identification is performed by communications service providers, then traffic flow identification can be done, but the process becomes expensive
Solution Approach 1:
The patent replaces expensive manual labor with automated machine learning systems. The automated ML-based traffic identification system processes large volumes of intercepted messages without requiring human operators, significantly reducing operational costs while maintaining or improving identification accuracy.
Solution Approach 2:
The system enables self-service automated traffic identification where the ML models autonomously analyze and classify traffic flows without requiring continuous human intervention. The system automatically adapts and improves through continuous learning from intercepted data, reducing ongoing operational expenses.
3Measurement precision
If manual traffic identification is performed, then traffic flow identification can be done, but misunderstandings occur between CSP and LEA regarding data formatting correctness
Solution Approach 1:
The patent standardizes the formatting parameters of intercepted data through automated processing. The system applies consistent formatting rules and data structures when preparing intercepted traffic for delivery to law enforcement agencies, eliminating variations that cause misunderstandings between CSPs and LEAs.
Solution Approach 2:
The system incorporates feedback mechanisms where the automated processing results are validated and compared against expected formats and patterns. This feedback loop ensures data formatting consistency and allows the system to correct formatting issues automatically, reducing discrepancies between CSP and LEA expectations.
4Measurement precision
If manual verification of intercepted data is performed, then data correctness can be checked, but it involves handling of a huge number of files making it very expensive
Solution Approach 1:
The patent replaces expensive manual verification of intercepted data files with automated ML-based validation systems. The system automatically verifies data correctness, formatting, and completeness through algorithmic checks, eliminating the need for human operators to manually examine large numbers of intercepted files.
Solution Approach 2:
The system performs selective verification focusing on critical data elements and patterns rather than examining every single file in detail. The ML models identify and verify only the most important aspects of intercepted traffic, achieving effective verification with reduced computational and operational resources.
Data Source
AI summary
Automatic preparation of data related to session initiation protocol (SIP) based traffic flows in a lawful interception (LI) scenario is disclosed. The dataset that is obtained may, e.g., be used for machine learning-based (ML) and artificial intelligence (AI) tools that can identify lawfully intercepted SIP-based traffic cases. Such preparation of data reduces the 5 risk of misunderstandings between a communications service provider (CSP) and a law enforcement agency (LEA), which reduces the time dedicated by both parties in understanding the correctness of LI data provided by the CSP to the LEA.