Applying ML Models to Detect Anomalies in Containers, Serverless Functions, and Microservices

Abstract

This research explores the use of machine learning models to identify anomalies in containers, serverless functions, and microservices. The study utilizes explanatory research design and secondary data sources to investigate the relationship between intelligent detection systems that increase the security and reliability of the operation. The results indicate the ML-based anomaly detection enhances incidence response time, minimizes false positives, and enhances system uptime. The study underlines the increasing importance of ML in the process of automation of threats detecting and suggests the necessity of qualified workers, an efficient infrastructure, and collaboration with other countries.