Machine learning (ML)-based API protection: ML-based API protection helps detect and mitigate abnormal behavior in API traffic, such as bot attacks or abuse, by learning and adapting to normal traffic patterns.
API schema validation: API schema validation ensures that the API requests conform to the defined schema (e.g., checking the structure, fields, and types in the API calls). This helps prevent attacks like XML or JSON injection by ensuring only valid requests are processed.
API user key enforcement: Enforcing API user key authentication requires clients to provide valid API keys, ensuring only authorized users can access the API. This is crucial for controlling access to the API.

In the SVM plot of potential bot activity, you can see that the sample value (orange) is significantly different from the average value (green) and the maximum value (blue) in most of the metrics. This suggests unusual or abnormal behavior, indicating that the connection might be a bot. Typically, bots exhibit patterns that diverge from normal user activity, such as higher frequencies of certain types of requests, abnormal throughput, or an unusual pattern of HTTP requests (such as requests without referers or excessive TCP connections).