Model-agnostic meta-learning framework for data loss detection with transfer learning

Model-Agnostic Meta-Learning (MAML) has proven to be effective in various learning environments. However, it faces challenges with domain adaptation because it depends on gradient-based optimization, which does not explicitly integrate prior knowledge from related tasks. This limitation results in slow adaptation to new domains and suboptimal performance when Signiant domain shifts occur. Utilizing transfer learning, which skillfully incorporates domain-specific knowledge to boost generalization and adaptability, effectively resolves the challenges faced by current MAML-based techniques across various environments. This study explores the use of transfer learning to create a strong and flexible model that can effectively detect occurrences of data loss or leakage within credit cardholder information datasets. The model is trained on a source domain and ne-tuned on a target domain relevant to data loss detection by leveraging transfer learning. The effectiveness of the Transfer Learning-Based Data Loss Detection on MAML is evaluated through learning iteration versus mean squared error plots. The proposed system also surpasses the existing few-shot learning-based MAML. These plots provide insights into the model's convergence, adaptability, and performance. The abstract highlights the signicance of transfer learning in enhancing the efficiency and accuracy of data loss detection systems, particularly when utilizing the MAML is evaluated through learning iteration versus mean squared error plots. The proposed system also surpasses the existing few-shot learning-based MAML. The findings contribute to the expanding knowledge of transfer learning applications in cybersecurity and data protection. Experiments conducted on the IEEE-CIS Fraud Detection dataset demonstrate that our approach achieves an accuracy of 92.3% and a notable reduction in MSE by 15% compared to standard MAML, underscoring its effectiveness and robustness across various environments.