CNN-BiLSTM ve Momentum ile Optimize Edilmiş SGD Kullanılarak Gelişmiş Mobil Para Dolandırıcılığı Tespiti

Abstract

Mobil para sistemlerinin hızla benimsenmesi, dolandırıcılık faaliyetlerinde önemli bir artışa yol açarak güvenliklerini ve itibarlarını tehlikeye atmıştır. Bu araştırma, Stokastik Gradyan İnişi (SGD) kullanarak momentumla bir CNN-BiLSTM modelini değiştirerek mobil para dolandırıcılığını tespit etmek için gelişmiş bir yöntem sunmaktadır. Önceden işlenmiş bir hibrit CNN-BiLSTM modeli kullanarak işlem verilerinden belirgin özellikleri hesapladık ve modeli coğrafi ve zamansal yönleri içeren verilerdeki eğilimleri belirlemek üzere eğittik. Model, endüstri standardı test yaklaşımlarını kullanarak dikkat çekici bir performans gösterdi: 0.9928'lik bir F1 puanı, 0.9927'luk bir hassasiyet, 0.9928'lık bir doğruluk ve 0.9929'lık bir geri çağırma. Önerilen model, sahtekârlığı tespit edebilir ve düşük bir yanlış pozitif oranına sahiptir. Çalışmaya göre, model özellik seçimini iyileştirir ve çeşitli optimizasyon tekniklerini birleştirerek onu daha esnek ve farklı mobil para sistemleri için uygun hale getirir.

Keywords

• Sahtecilik Tespiti
• Makine Öğrenmesi
• Sinir Ağları
• Stokastik Gradyan İnişi

Advanced Mobile Money Fraud Detection Using CNN-BiLSTM and Optimized SGD with Momentum

Abstract

The accelerated adoption of mobile money systems has significantly increased fraudulent activity, compromising their security and trustworthiness. This research presents an enhanced method for detecting mobile money fraud by modifying a CNN-BiLSTM model with momentum using Stochastic Gradient Descent (SGD). We computed salient features from transaction data using a pre-processed hybrid CNN-BiLSTM model and trained the model to identify trends in the data that included geographical and temporal aspects. The model performed remarkably using industry-standard testing approaches: an F1 score of0.9928, precision of 0.9927, accuracy of 0.9928, and recall of 0.9929. The proposed model can identify dishonesty and has a low false positive rate. According to the study, the model improves feature selection and incorporates various optimization techniques, making it more flexible and suitable for different mobile money systems.

Keywords

• Fraud Detection
• Machine Learning
• Neural Networks
• Stochastic Gradient Descent

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