Microservice architectures enable the continuous delivery and deployment of complex distributed systems. With this architecture choice comes the challenge of connecting, managing, and securing communications between the various microservices. Recent application level denial of service attacks which target backend services have demonstrated a rising attack vector that is capable of bringing down enterprise level systems with minimal cost and effort. By targeting expensive services using readily available cloud computing services, a single user is capable of backing up an entire system and causing widespread outages.

This paper seeks to research and better understand the feasibility of using machine learning models to analyze traffic between microservices in order to perform anomaly detection in the service mesh. The goal of this research is to utilize these models to perform real time policy enforcement decisions based on changes in requests between services.