Credit Card Fraud (CCF) detection is a major challenge in financial security, especially in detecting unauthorized transactions. As fraudsters' patterns increase, traditional methods face challenges in detecting them. Hence, this paper proposes a Sea Lion - Self-Supervised Network (SL-SSNet) to improve detection accuracy. This research study aims to enhance performance by optimizing data quality feature extraction and achieving better results in fraud detection. The innovative approach for CCF detection using a hybrid optimization model combines the strengths of Sea Lion optimization and Self-Supervised Networks to improve both model accuracy and performance. Initially, the CCF dataset is collected from Kaggle. Then the data goes through a pre-processing phase where irrelevant data points, noise, and low-quality data are removed. The relevant data is selected in the next phase. Feature extraction is performed to select the most important and influential features related to fraud detection. The final phase is prediction and performance evaluation. The results show that the SL-SSNet model performs better than other methods in fraud detection. Specifically, the model achieved 99.98% accuracy, 82.46% precision, 97.23% recall, and 89.97% F1-score. These results prove the effectiveness and robustness of SL-SSNet in detecting fraudulent transactions.

In the rapidly evolving landscape of blockchain technology, accurately classifying blockchain addresses is crucial for enhancing security, tracking illicit activities, and understanding transaction behaviors. This paper presents a novel classification method leveraging a Graph Neural Network (GNN) framework, which utilizes an account balance model to analyze relationships and transactions between blockchain addresses. By constructing a graph representation of the blockchain network, we capture both the structural and functional attributes of addresses. Our approach demonstrates significant improvements in classification accuracy compared to traditional methods, offering a robust solution for identifying various address types, including exchanges, personal wallets, and service providers. Experimental results show that our model effectively distinguishes between different classes of addresses, thereby contributing to the broader field of blockchain analytics and security.

To address the challenges of data security governance under new conditions and align with technological trends in data security, this paper proposes a graph algorithm-based method for identifying key personnel with critical permissions in the practical applications of large state-owned enterprises. This method can uncover potential permission influence factors within the system and evaluate the weight of influence from different perspectives, providing highly interpretable identification results. To tackle the issue of detecting anomalous user and entity behaviors in data security governance, a user and entity behavior anomaly detection method based on Generative Adversarial Networks (GAN) is introduced. Experimental results show that the proposed method achieves higher precision, recall, and F1-score averages compared to baseline models; specifically, an average F1-score of 0.75 versus 0.72 for LSTM-based TadGAN, 0.62 for ARIMA, and 0.65 for a commercial UEBA baseline across the three evaluation datasets. A data security platform was designed and developed, which plays a significant role in reducing data security risks, assisting enterprise compliance, and promoting data development and utilization. This platform has been applied in various centralized data management projects and meets the big data processing requirements in secure environments, demonstrating strong application and promotional value.

Blockchain technology allows decentralized cryptocurrencies to change digital finances by providing secure, pseudonymous transactions to users. Since blockchain ledgers operate in a public environment, users can face potential privacy risks due to the exposure of their transaction patterns. Conventional cryptocurrency systems use block generation for transaction confirmation, yet this process produces latency and impacts the real-time efficiency of transactions. This paper develops a proxy-assisted cryptocurrency payment system that employs blind signature principles to achieve better system privacy and enhanced speed. The core functionality of this proposed system aims to protect transaction secrecy as it speeds up confirmation processes. A proxy node handles transaction requests through blind signature protocols that guarantee data confidentiality as part of the methodology. The proposed system utilizes deep learning tools, which include recurrent neural networks (RNN), graph neural networks (GNN), and reinforcement learning (RL) to forecast confirmation results, identify scams, and control proxy functions dynamically. Research indicates that the introduced method substantially boosts privacy features, decreases transaction latencies, and enhances the security of all transactions by providing an encouraging roadmap for secure cryptocurrency systems that preserve privacy.