Advanced Threat Detection in API Security: Leveraging Machine Learning Algorithms

Abstract

The use of APIs in the current software environments has amplified the vulnerability of APIs in the face of cybsecurity risks. Since APIs are in-between interfaces between the systems, it becomes a common approach for attackers to leverage the available flaws and weaknesses in them. Anti-virus softwares such as firewalls and signature IDS fail to counter enemy easy-to-modify and more complex API attacks. This paper aims to focus on how Machine Learning (ML) algorithms can be used to identify and prevent advanced threats in API security. To address the challenges involved in cyber threat detection, we have developed a general framework combining supervised and unsupervised with targeted reinforcement learning for anomaly, intrusion, injection attacks, DDoS and authentication bypass. Let me analyze some of the machine learning algorithms that can be applied and applied to enhancethe real-time threat detection process for cloud computing networks, including decision trees, random forests, artificial neural networks and models based on deep learning. Finally, we talk about the problems of applying ML models for API security, for example, how to deal with high dimensional data, how to minimize false positive results, and how to protect from newly emerged threats. In order to assess our approach, we used publicly available datasets and simulated API traffic, thus minimizing the false negative rate and enhancing the detection of zero-day exploits. The present findings also show the usefulness of additional machine learning approaches for predictive and responsive API protection over traditional procedures. Additionally, issues concerning dataset quality, feature extraction, and real-time deployment are raised in explaining ways through which ML-based threat detection systems can only be successful. Last, we offer suggestions for future research to advance API security in distributed systems through federated learning and to incorporate explainability in detection methods using xAI.