## 6 Conclusions and Future Research Directions  
The rising global adoption of credit cards has made them a preferred and commonly used payment option for daily transactions, exerting a significant influence on global financial cybersecurity. This study introduces a credit card fraud detection (CCFD) system employing blockchain-federated learning, which integrates federated learning (FL) with blockchain technology. Through the integration of FL and blockchain techniques, our system guarantees improved privacy, enhanced data protection, and minimized risk of data breaches. Additionally, the integration of FL and blockchain in credit card services ensures preserved privacy, data protection, decentralized storage, secure payment networks, and automated tasks. Three machine learning and deep neural network algorithms, RF, CNN, and LSTM, are utilized, alongside three optimization techniques: ADAM, SGD, and MSGD. Furthermore, the SMOTE oversampling technique is employed to balance the dataset before model training. The proposed framework has proven effective in enhancing classification performance and prediction accuracy. Despite the vast number of credit card fraud detection (CCFD) systems and frameworks proposed in academic and industrial fields, numerous challenges and limitations adversely affect their efficiency and effectiveness. These limitations require greater attention for resolution, including issues such as imbalanced data, adversarial attacks, feature engineering, real-time detection, cost of false positives, and data privacy. The imbalance of data is a critical issue affecting prediction accuracy due to class distribution disparities. Fraud continuously evolves, employing new attacks and fraud methods that can deceive CCFD systems and evade the detection of new and unseen fraudulent transactions. Fraudsters utilize adversarial attacks, including data poisoning, evasion attacks, and input data manipulation, to deceive the model. Developing a real-time detection system is crucial for fraud detection, but it becomes challenging in high-volume environments and with big data. Any delay in fraud detection can result in financial losses for both cardholders and financial institutions. Moreover, an increase in false alarms can negatively impact the accuracy and integrity of CCFD systems, leading to potential issues for individuals and financial institutions. Finally, data privacy and protection are critical aspects that warrant increased attention. However, due to these concerns, finding publicly available data for conducting experiments presents a significant obstacle for CCFD system developers. In future work, additional efforts will be directed toward maintaining privacy and protecting data by implementing defensive measures against potential threats. Our objective is to further improve privacy and data protection by deploying a defensive system capable of detecting and preventing potential attacks or instances of fraud in real-time. Our next project involves implementing an online credit card fraud detection (CCFD) system that simulates various attacks and instances of fraud, followed by an evaluation of the system's performance. We will assess its ability to prevent, detect, and mitigate fraudulent transactions by identifying attack patterns. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Metrics**} \\ \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline BMR [62] & 0.69 & 0.66 & 0.71 & 0.68 \\ AATE [63] & 0.70 & 0.66 & 0.71 & 0.69 \\ PD-FDS [64] & 0.70 & 0.68 & 0.74 & 0.71 \\ SPD [65] & 0.74 & 0.69 & 0.82 & 0.75 \\ CMAB [66] & 0.81 & 0.71 & 0.86 & 0.78 \\ RawLR [67] & 0.81 & 0.82 & 0.91 & 0.86 \\ RMNLS [68] & 0.83 & 0.89 & 0.92 & 0.90 \\ Flow-Scope [69] & 0.87 & 0.89 & 0.93 & 0.91 \\ FD-Meta [58] & 0.99 & 0.98 & 0.99 & 0.99 \\ Approx-SMOTE [57] & 0.98 & 0.98 & 0.97 & 0.98 \\
**Our work** & **0.97** & **0.97** & **0.96** & **0.97** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison with other methods.