To help trace fraudulent activities or gather forensic evidence, telecom service providers need a solution that can not only identify relevant OTT service application data but also reconstruct the phone numbers used in OTT calls, revealing the parties involved in the communication. For OTT call analysis, beyond identifying OTT applications, the solution must include these key features:

• OTT traffic identification: Accurately detect OTT services (e.g., WhatsApp, LINE, Telegram) and distinguish communication types like voice, video, and messaging.
• OTT call user identification: Trace and reconstruct phone numbers or device IDs from OTT call traffic to help identify users, including details like MSISDN, IMEI, IMSI, cell tower ID, and tower location, etc.
• Data retention and analytics: Support robust data storage and retrieval, offering detailed reports for investigative purposes.

This solution would significantly improve law enforcement’s ability to track and reconstruct communication data in OTT environments, enabling more effective fraud and criminal investigations. However, providing OTT call analysis features faces several technical challenges:

• Variety of OTT technologies: The diverse technologies used by various OTT services for call initiation and reception make it difficult to extract accurate caller and callee information from large volumes of network traffic. For instance, identifying caller and callee details becomes complex when calls are established using P2P, relay servers, hybrid or server-side combination modes.
• The impact of private IP addresses: Carrier-Grade Network Address Translator (CGNAT) inherently complicates the identification of communication source and destination IPs, as well as user identities. In the context of OTT calls, the use of STUN (Session Traversal Utilities for NAT) or TURN (Traversal Using Relays around NAT) servers under different deployment scenarios adds another layer of complexity to accurate identification.
• Data analysis and performance challenges: Identifying OTT traffic and applications within carrier-grade networks requires continuous, in-depth monitoring of network traffic which generates immense data volumes. This imposes significant challenges on data collection, processing, visualization, and storage efficiency, demanding advanced capabilities to maintain performance and reliability at scale.