This document discusses using Amazon SageMaker and GuardDuty to identify anomalous traffic through machine learning. It provides an overview of the goals, agenda, and key components involved. The goals are to demonstrate machine learning for security, show fundamentals of AWS services like CloudTrail and GuardDuty, and discuss building a model in SageMaker. The agenda involves using the AWS Jam platform, reviewing GuardDuty findings, preprocessing log data, training a model in SageMaker, and fusing results. Key components discussed are CloudTrail logs, GuardDuty findings and data sources, and the SageMaker workflow.