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Operationalizing machine learning based security detections is extremelychallenging, especially in a continuously evolving cloud environment.Conventional anomaly detection does not produce satisfactory results foranalysts that are investigating security incidents in the cloud. Modelevaluation alone presents its own set of problems due to a lack of benchmarkdatasets. When deploying these detections, we must deal with model compliance,localization, and data silo issues, among many others. We pose the problem of"attack disruption" as a way forward in the security data science space. Inthis paper, we describe the framework, challenges, and open questionssurrounding the successful operationalization of machine learning basedsecurity detections in a cloud environment and provide some insights on how wehave addressed them.

Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward