Browsing by Author "Fikirte Endalew"

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    Deep Learning Based SIMBox Fraud Detection using CDR data: A case of Safaricom Ethiopia
    (Addis Ababa University, 2024-06) Fikirte Endalew; Elefelious Getachew (PhD)
    The telecommunications industry is a critical component of modern society, facilitating communication and data exchange across individuals and businesses globally. However, this interconnectedness also presents vulnerabilities that malicious actors exploit. In the telecom sector, fraud usually refers to deliberate misuse of voice and data networks as well as service theft. One of the most difficult problems telecom organizations worldwide have is SIMBox fraud. A SIMBox fraud diverts international calls to a cellular device through the internet via a device called a SIMBox, routing telecom services to local networks into the network as local services, using hundreds of low-cost or even unpaid SIM cards, which are often obtained with forged identities. Ethiopia’s distinctive ethnolinguistic, cultural, and socioeconomic landscape significantly shapes its Call Detail Record (CDR) data. To effectively detect SIM Box fraud within this context, it is imperative to develop fraud detection models that are specifically tailored to the Ethiopian telecommunications environment. Detecting fraud activities becomes increasingly challenging as the number of subscribers and CDR log volumes and velocities increase. In order to identify telecom fraud via data mining techniques, deep learning techniques have become more and more popular in the telecom sector and other domains in recent years. While Machine Learning algorithms demonstrate effectiveness in detection, a fundamental challenge lies in balancing speed with accuracy. This challenge requires a careful balance between the two, as optimizing one metric often compromises the other. Telecom operators are facing financial losses due to SIM box fraud. Early detection of these fraudulent activities is critical to minimize revenue leakage. Therefore, evaluating the effectiveness of various fraud detection systems is essential to ensure a swift response. In this thesis, a CRISP-DM based methodology is followed to collect, discover, preprocess and model as well as evaluate CDR based SIMBox fraud detection for Safaricom Ethiopia. BERT, MLP, LSTM and classic rRNN deep learning models are implemented with evaluation. The results show that the rRNN algorithm with GRU architecture showed the highest accuracy of 99.7% followed by LSTM, BERT and MLP at 99.1%,98.6% and 96.7% of accuracy respectively.