An Efficient Machine Learning-based Framework for Detection and Prevention of Frauds in Telecom Networks
Telecommunication fraud is an acute problem that leads to substantial material losses and compromises the reliability of telecom systems worldwide. Only effective and efficient detection mechanisms can help to deal with these threats, though there are certain shifts in the approaches to fraud detection. This paper evaluates the performance of AI-driven models for fraud detection in telecommunication networks using Call Detail Record (CDR) datasets. This study focuses on fraud detection in telecom networks using the Telecom CDR dataset, which contains 101,174 customer records with 17 attributes, including 8,830 fraud cases. In feature preprocessing, missing values were dealt with, followed...
| Authors | Praveen Hegde, Mishal Shah |
|---|---|
| Primary category | cs.NI |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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