# Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards
## 5 Results and Discussion
### The Hybrid ML+DL  
Initially, we evaluate the performance of the novel hybrid stacking ML+DL model without utilising any sampling techniques. The results from this evaluation are thoroughly detailed in Table 6. Additionally, Figure 9 presents the confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline
**Hybrid stacking ML+DL** & 99.97\% & 97.62\% & 83.67\% & **90.11\%** & 91.83\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Hybrid ML+DL model performance.  
Figure 8: Confusion matrix of BiLSTM model.  
The novel hybrid ML+DL model exhibits outstanding performance across several assessment measures. With an accuracy of 99.97%, the model showcases its ability to accurately classify instances, which is crucial in credit card cyber fraud detection where even minor errors can have significant consequences. Additionally, the model achieves a high precision of 97.62%, indicating a low rate of false positives. This is particularly advantageous in cyber fraud detection, where correctly identifying fraudulent transactions is paramount to minimising financial losses for both customers and financial institutions.  
Moreover, the model exhibits a commendable recall of 83.67%, highlighting its capability to capture a high proportion of actual positive cases. In cyber fraud detection scenarios, where the number of fraudulent transactions is typically much lower than legitimate ones, a high recall ensures that the model effectively identifies fraudulent activities, thereby enhancing cyber fraud detection efficiency. The F1 score of 90.11% further emphasises the model's balanced performance between precision and recall, demonstrating its ability to manage false positives and false negatives. The AUC value of 91.83% underscores the model's ability to distinguish between fraudulent and legitimate transactions effectively. A high AUC suggests that the model performs well across various threshold values, further reinforcing its reliability in making accurate predictions.  
The novel hybrid ML+DL model excels in its intricate design, which effectively integrates the advantages of both classic ML and DL techniques. The ML techniques incorporated, such as DT, RF, SVM, XGBoost, CatBoost, and LR, offer a diverse set of tools tailored to address specific challenges in credit card cyber fraud detection. These techniques leverage various algorithms and strategies to effectively capture patterns and make accurate predictions. Furthermore, the DL techniques utilised, including CNN and BiLSTM networks, are well-suited for handling sequential data such as transaction sequences. The integration of attention mechanisms further enhances the model's ability to focus on relevant segments of the input sequence, improving interpretability and performance. Overall, the novel hybrid ML+DL model benefits from comprehensive feature extraction, stability, efficiency, dynamic attention mechanisms, robustness, and generalisation capabilities. It is an effective tool for addressing intricate binary classification challenges, particularly in credit card cyber fraud detection scenarios, due to its high-level architecture, meticulous layer sequencing, and robust algorithmic components.  
Figure 9: Confusion matrix hybrid ML+DL model.