Machine Learning for Securing API Gateways : a Systematic Literature Review

The rapid growth of mobile banking has improved access to financial services but also introduced heightened cybersecurity risks, particularly due to vulnerabilities in API Gateways and limited user awareness of cyber threats. This study conducts a Systematic Literature Review (SLR) to explore how machine learning (ML) can address both technical and human-centric security challenges in digital banking. By reviewing sixteen peer-reviewed studies published between 2019 and 2025, the study identifies key ML techniques such as anomaly detection, behavior-based models, and deep learning architectures that are effective in detecting and mitigating API-based attacks. In parallel, the review examines ML applications aimed at enhancing user cybersecurity awareness, including personalized alert systems, user segmentation, and adaptive education mechanisms. Thematic synthesis reveals several challenges, including data availability and privacy, the interpretability of complex models, and integration with existing banking infrastructures. However, the study also highlights significant opportunities, such as the use of federated learning to preserve privacy, explainable AI to improve trust, and dynamic alert systems to prevent user fatigue. Based on the synthesis, a conceptual architecture is proposed to integrate ML-driven API threat detection and user education within mobile banking platforms. The findings provide valuable insights for both academic research and practical implementation, contributing to the development of intelligent, user-aware cybersecurity frameworks in the financial sector.Keywords: API Gateway Security, Cybersecurity Awareness, Machine Learning, Mobile Banking, Systematic Literature Review.