Route Server Fraud Detection in VoIP Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
VoIP service providers face significant financial losses due to fraudulent traffic, which is difficult to detect and manage, as malicious actors can send fraudulent calls that are not paid for by originating gateway operators, leading to contractual liabilities and revenue loss.
Innovation Solution
Implementing a route server computer with unwanted-call detection programming that examines call statistics in real-time to identify patterns of fraudulent activity, blocks or warns against suspicious calls, and integrates with Session Initiation Protocol (SIP) and other network queries to prevent fraudulent traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time call statistics examination is implemented to detect fraudulent traffic, then fraud detection capability is improved, but system complexity and processing load increase
Solution Approach 1:
The fraud detection system is segmented into multiple independent components: a call statistics examination module that analyzes call data, a fraud detection module that identifies fraudulent patterns, and a call routing module that handles legitimate calls. This segmentation allows each module to perform its specific function efficiently without overwhelming the entire system, resolving the contradiction between improved fraud detection and reduced system complexity.
Solution Approach 2:
A call statistics database serves as an intermediary layer between the call routing system and the fraud detection system. Call statistics are stored and managed in this database, allowing the fraud detection module to query and analyze data without creating direct complex interactions with the call routing processes. This intermediary structure improves fraud detection capability while maintaining system manageability.
2Measurement precision
If comprehensive call statistics are collected and analyzed, then fraud detection accuracy is improved, but information processing time and computational load increase
Solution Approach 1:
Call statistics are collected and stored in a database in advance, before fraud detection is needed. This preliminary action of data collection and organization allows the fraud detection module to quickly query and analyze pre-processed data when fraud detection is required, improving detection accuracy without incurring excessive processing delays during actual fraud detection operations.
3Loss of energy
If fraudulent calls are blocked in real-time, then revenue loss is reduced, but legitimate traffic may be inadvertently affected
Solution Approach 1:
The system implements feedback mechanisms where call statistics are continuously monitored and analyzed, and blocking decisions are based on real-time fraud detection results. The system can adjust its behavior based on the detected patterns, allowing legitimate traffic to pass through while blocking fraudulent calls. This feedback-driven approach reduces revenue loss from fraud while maintaining flexibility in traffic flow management.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method that detects unwanted calls based on information shared between a first telecommunications network provided by a first service provider and a second service provider providing a second telecommunications network including receiving, by a data-processing system, a request to provide a telecommunications service to the first telecommunications network, the request being received by the first telecommunications network from a user of the first telecommunications network through a first gateway, and wherein the request comprises a calling party identification that identified a calling party and a called party identification that identified a called party. The method further includes examining, by the data-processing system, statistics relating to requests for telecommunications services; and generating, by the data-processing system, a first signal when the called party identification in the request appears in the statistics in excess of a predetermined first number of instances within a predetermined first time interval.