# Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards
## 2 Related Works
### Deep Learning  
DL is a subset of ML that focuses on analysing data through hierarchical feature extraction. The key advantage of DL is its ability to automatically learn features without the need for manual feature selection [27]. DL algorithms such as CNN and LSTM are associated with image processing and Natural Language Processing (NLP). Using these methods for credit card cyber fraud detection has yielded better performance than traditional algorithms [28]. CNNs have demonstrated their efficacy in successfully processing multi-channel data, therefore enabling a thorough analysis of transaction information. Ref. [29] used DL techniques such as CNN, BILSTM with an Attention Layer to classify illegitimate transactions. The CNN-Bi-LSTM-ATTENTION model is highly effective in identifying fraudulent classes. Analysis indicated that the model was adequate and yielded an accuracy of 95%. Ref. [30] introduced a credit card cyber fraud detector that was optimised for large-scale real-time datasets and utilised a CNN combined with a smart matrix algorithm. Compared to alternative ML approaches, the performance of the three-layered CNN model is superior. Ref. [31] suggested a hybrid CNN-SVM model for the detection of fraudulent credit card transactions. The experimental findings indicate that the hybrid CNN-SVM model achieved classification performances of 91.08%, 90.50%, 90.34%, and 90.41, respectively, in terms of accuracy, precision, recall, and F1-score. LSTM is a helpful technique for predicting cyber fraud because of its historical knowledge and the link between prediction outputs and historical input. Ref. [32] developed a new model to improve both the present detection techniques and detection accuracy considering large amounts of data. The findings demonstrated that LSTM performed perfectly, achieving 99.95% accuracy. Ref. [33] recommended a model to record the previous purchasing behaviour of card holders. The results showed that the LSTM model achieved a high level of performance. Ref. [34] proposed a new model as a means of mitigating misclassification in cyber fraud detection systems. The application of an LSTM-RNN was implemented. A comparison of the obtained results to previous research revealed that this model achieved both a high rate of accurate classification and a low rate of false alarms. Ref. [35] introduced a novel hybrid model with the objective of identifying the occurrence of credit card cyber fraud. RNN-LSTM and an attention mechanism have been proposed. Comparing the performance of RNN-LSTM to that of ANN, XGBoost, RF, NB, and SVM classifiers reveals that the proposed model generates robust results with an accuracy of 99.4%. BiLSTM models were employed to analyse data in both forward and backward directions. These models utilise LSTM cells equipped with memory units that enable them to record temporal patterns and relationships [36]. Ref. [37] introduced the Hybrid Sampling (HS)-Similarity Attention Layer (SAL)-BiLSTM architecture. By hybrid sampling of the minority class and undersampling of the majority class, the SMOTE-ENN decreases data discrepancy. SAL is implemented to quantify the similarity of a data sequence to assign importance to distinctive features and mitigate the issue of overfitting in classification. The recall value of the proposed HS-SAL-BiLSTM was 99.2%, whereas that of the existing RF-SMOTE-SVM was 97.7%. A DL-based hybrid approach for detecting fraudulent transactions was applied by [38]. A Bi-LSTM autoencoder with an isolation forest is incorporated into the new model. This model suggests that fraudulent transactions can be detected at a rate of 87%. The academic literature highlights various Machine Learning (ML) and Deep Learning (DL) methodologies applied to credit card cyber fraud detection [39]. In recent years, there has been a notable increase in the exploration of DL techniques, which offer flexibility in responding to complex data patterns and detecting new fraudulent behaviours.