Machine Learning (ML)

Machine learning is the branch of artificial intelligence in which systems learn statistical patterns from data rather than being explicitly programmed. Telecom applications include signalling anomaly detection, fraud scoring on call detail records, RAN self-optimisation, predictive maintenance, and customer-experience analytics. ML systems themselves require security attention against adversarial inputs and data-poisoning.

Categories: Radio Access NetworkCore NetworkFraud

Machine Learning (ML) in context

The radio access network is where mobile devices attach to the operator's infrastructure. Attacks in this layer include IMSI catching, rogue base stations and downgrade attacks; defenses rest on mutual authentication, integrity-protected signaling and Open RAN supply-chain hygiene.

The mobile core carries subscriber sessions, mobility and policy. In 4G it is the EPC (MME, HSS, S/PGW); in 5G it is the Service-Based Architecture with AMF, SMF, UPF, AUSF, UDM and the NRF.

To place Machine Learning (ML) in the wider telecom-security picture, review 5G, 5G SA, 5G NSA, A5/3, AMF and AMPS — each entry cross-references back to this page so you can walk the topic in either direction.

Related terms

More from the TelcoSec Glossary

Browse the full TelcoSec Glossary, the Ultimate Guide to Mobile Network Security, or the P1 Arsenal of telecom-security tools.