This is the scenario:
Client -> Server
The client sends multiple voice calls to Server. Call info:
- calling number
- called number
- call duration
- source IP
- destination IP
- SIP header information (User-Agent, Version, Proxy information)
Numeric Patterns - Destination.
Time Patterns - Calls occurring too regularly, activity at unusual times or dates.
Geographic Patterns - Proximity relationships between apparently unrelated entities.
In case of Toll fraud and client being insecure, the attacker can send calls via original agent hence the Service Provider won't reject calls immediately at least based from IP information. (Toll fraud). I'm exploring which approach is the best to implement an ML model, instead of static rules to be able to detect Toll fraud in a live system:
- Classification (Fraud or not)
- Reinforcement Learning (If fraud then reward)
I'm new to RL, does this scenario makes sense for live systems? Can I use offline data to train an RL model? Offline data includes Fraud cases detected historically.