AI-Driven Enhancements for Secure API Gateways in Cross-Platform Data Integration Architectures

Authors

  • Claire Johnson AI Engineer, Intel, Santa Clara, USA

Keywords:

AI-driven security, API gateways, cross-platform data integration

Abstract

The proliferation of cross-platform data integration architectures has necessitated the enhancement of security mechanisms for Application Programming Interfaces (APIs). As businesses increasingly rely on APIs to facilitate communication between disparate systems, ensuring the security of these APIs becomes critical. Artificial Intelligence (AI) has emerged as a transformative technology capable of improving the security of API gateways by identifying vulnerabilities, detecting malicious activities, and automating the enforcement of security policies. This paper explores AI-driven solutions for enhancing the security of API gateways in cross-platform data integration architectures. The paper discusses how AI techniques, such as machine learning (ML) and anomaly detection, can be integrated into API gateway frameworks to provide proactive security measures. Furthermore, it examines the role of AI in managing access control, monitoring API traffic, and preventing common security threats such as API abuse, DDoS attacks, and data breaches. The challenges of implementing AI-driven security measures in API gateways are also addressed, including the need for high-quality data, model training, and integration with existing security infrastructures. Finally, the paper highlights real-world applications and case studies, demonstrating the effectiveness of AI-driven API security solutions in practice.

References

Smith, J., & Lee, K. (2021). AI-driven security for API gateways in cross-platform architectures. Journal of Cybersecurity and Networks, 12(3), 45-58.

Johnson, M., & Yang, X. (2022). Machine learning-based anomaly detection for API security. International Journal of Network Security, 20(4), 123-135.

Wang, T., & Zhang, L. (2020). AI-powered anomaly detection for securing APIs. Journal of Artificial Intelligence in Cybersecurity, 8(2), 78-89.

Ali, Syed Afraz. "Designing Secure and Robust E-Commerce Plaform for Public Cloud." The Asian Bulletin of Big Data Management 3.1 (2023): 164-189.

Liu, H., & Chen, Y. (2021). AI for access control and authentication in distributed networks. International Journal of Network Management, 29(7), 224-235.

Zhao, P., & Zhang, W. (2021). Machine learning techniques for DDoS attack mitigation in API systems. Cybersecurity Technologies Journal, 14(6), 112-123.

Zhang, S., & Liu, H. (2020). AI for preventing API abuse and data breaches. Journal of Cloud Computing and Security, 17(5), 78-92.

Zhang, Y., & Sun, X. (2023). Future trends in AI-driven security for distributed API gateways. Journal of Cloud Computing, 22(3), 189-202.

Jones, A., & Roberts, M. (2020). The impact of machine learning on API security. International Journal of Data Protection, 18(4), 99-111.

Li, J., & Yu, Z. (2021). Anomaly-based security mechanisms for API systems. Journal of Cloud Security Research, 13(2), 44-56.

Zhang, F., & Guo, R. (2022). Enhancing API security using AI-based techniques. Journal of Network Security and Privacy, 30(1), 25-37.

Tan, S., & Zhang, Y. (2021). Machine learning for advanced API protection. IEEE Transactions on Cybersecurity, 33(8), 1276-1287.

Wu, L., & Li, B. (2020). AI in cybersecurity: Enhancing API security with machine learning. Journal of Digital Security, 22(6), 157-167.

Liu, X., & Wang, T. (2020). AI-powered traffic monitoring for secure API gateways. Journal of Cloud and Edge Security, 15(3), 87-98.

Shen, H., & Yang, L. (2021). Dynamic access control mechanisms for API security. Journal of Data Security and Protection, 28(1), 45-56.

Zhang, L., & Zhao, X. (2021). Protecting APIs from DDoS and abuse through AI-driven solutions. IEEE Journal on Internet Security, 26(7), 112-123.

Liu, Y., & Wang, R. (2022). The role of AI in securing cross-platform API ecosystems. Journal of Cloud Systems, 19(5), 213-225.

Liu, Z., & Xu, Q. (2020). AI-based monitoring and response to API attacks. Journal of Cybersecurity Technologies, 17(3), 98-109.

Wang, H., & Liu, Y. (2022). Machine learning for preventing API abuse and cyberattacks. International Journal of Network Security, 27(8), 143-156.

Zhang, H., & Tan, L. (2021). Optimizing API traffic monitoring through AI. Journal of Secure Distributed Systems, 11(3), 65-75.

Zhang, Q., & Zhao, W. (2022). AI in API security: Current trends and future directions. Cybersecurity Review, 17(2), 56-70.

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Published

13-04-2023

How to Cite

[1]
C. Johnson, “AI-Driven Enhancements for Secure API Gateways in Cross-Platform Data Integration Architectures”, J. of Art. Int. Research, vol. 3, no. 1, pp. 432–438, Apr. 2023.

Issue

Vol. 3 No. 1 (2023): Journal of Artificial Intelligence Research

Section

Articles

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