# Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards
## 5 Results and Discussion
### The Hybrid ML+DL with Sampling:  
The novel hybrid stacking ML+DL model employs a variety of resampling techniques to address the issue of class imbalance in our dataset. Class imbalance is a common issue in real-world applications such as cyber fraud detection [44], where the minority class (e.g., fraudulent transactions) is significantly underrepresented compared to the majority class [45]. To address this, we employ Borderline-SMOTE (Borderline Synthetic Minority Oversampling Technique), ROS (RandomOverSampler), Tomek Link, ENN, and SMOTEENN (SMOTE + ENN) as strategies to rebalance our dataset and enhance the performance of our hybrid stacking ML+DL model. _A. Borderline-SMOTE_  
In the novel hybrid stacking ML+DL model, Borderline-SMOTE addresses class imbalance by generating synthetic samples near the decision boundary, where misclassifications often occur. It focuses on difficult-to-classify minority class instances, enhancing the classifier's ability to learn clear and accurate decision boundaries [46]. This method is applied to the training data to balance classes before model training. _B. RandomOverSampler (ROS)_  
RandomOverSampler (ROS) balances the dataset by randomly duplicating minority class instances until both classes are equally represented. By providing a balanced training dataset, ROS helps the ML and DL components of the hybrid model learn more effectively. _C. Tomek Links_  
Tomek Links improves class separability by removing ambiguous instances near the decision boundary. Identifying and eliminating pairs of nearest neighbours from different classes reduces noise, helping the classifier focus on more distinct class patterns. _D. Edited Nearest Neighbours (ENN)_  
ENN cleans the dataset by removing noisy or ambiguous instances, especially near decision boundaries. It iteratively eliminates misclassified instances based on their k nearest neighbours, ensuring the dataset emphasises clear class boundaries. This refinement enhances the model's ability to detect minority class instances accurately. _E. SMOTEENN_  
SMOTEENN combines SMOTE and ENN to handle class imbalance. SMOTE generates synthetic minority class samples to balance the dataset, while ENN removes noisy or borderline instances, improving class distinction. This two-step process ensures the model is trained on a refined and balanced dataset, enhancing its robustness and predictive accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study Ref.** & **Model** & **Accuracy** & **F1 Score** & **Dataset** \\ \hline
[32] & LSTM & 99.95\% & — & European cards \\ \hline
[40] & BiLSTM & 91.37\% & — & European cards \\ \hline
[34] & LSTM-RNN & 99.58\% & 88.76\% & European cards \\ \hline
[29] & CNN-BiLSTM & 95\% & — & European cards \\ \hline
[28] & CNN & 99.72\% & — & European cards \\ \hline
[41] & CNN-ELM & 98.7\% & — & European cards \\ \hline
[42] & CNN & 99.81 & 83.72\% & European cards \\ \hline
[35] &
\begin{tabular}{c} RNN-LSTM- \\ Attention \\ \end{tabular} & 99.4\% & — & European cards \\ \hline
[43] &
\begin{tabular}{c} Hybrid \\ ML+BCBSMOTE \\ \end{tabular} & — & 85.20\% & European cards \\ \hline \hline
**NovelHybrid** \\
**ML+DL model** & Stacking ML+DL & 99.97\% & 90.11\% & European cards \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of performance with existing models. We employed several resampling techniques, oversampling, undersampling, and a combination of oversampling and undersampling, to address the significant class imbalance present in the dataset. Results from our hybrid stacking ML+DL model integrated with the several resampling techniques are shown in Table 8. Figure 10 shows the f1 score for the sampling techniques with the novel hybrid stacking ML+DL model. Figure 11 shows the performance of the sampling techniques with the novel hybrid stacking ML+DL model.