System and method of application programming interface (api) anomaly detection

A system for API anomaly detection, the system comprising:

• a non-transitory memory device, wherein modules of instruction code are stored, and at least one processor associated with the memory device, and configured to execute the modules of instruction code, whereupon execution of said modules of instruction code, the at least one processor is configured to:
• receive a target API request from at least one client computing device;
• infer a machine-learning (ML) based language model on the target API request, to generate a target API vector, representing an embedding of a text of the target API request in a textual embedding space;
• obtain a nearest neighbor model, comprising a plurality of baseline API vectors, each representing embedding of text of a respective baseline API request in the textual embedding space;
• search the nearest neighbor model, based on the target API vector, to select one or more baseline API vectors;
• calculate a similarity metric value, representing similarity of the target API request to the one or more baseline API requests, based on the target API vector and the selected one or more baseline API vectors;
• and
• determine anomaly of the target API request, based on the similarity metric value.