ProblemChild: Detecting living-off-the-land attacks using the Elastic Stack

The blog post discusses the ProblemChild framework, which aims to detect "living off the land" (LOtL) malware attacks using the Elastic Stack. These attacks exploit standard tools within a target environment to avoid detection, making them challenging to identify. The ProblemChild framework leverages machine learning to analyze Windows process event data and classify events as either malicious or benign. By extracting features from event metadata and training a supervised model, the framework enhances detection capabilities. It further applies anomaly detection to prioritize unusual events for analysts. The post outlines the process of feature engineering, model training, and event enrichment while emphasizing the importance of anomaly detection for refining the identification of rare malicious activities. The framework is tailored for Windows process events, with potential for expansion to other operating systems and event types in the future.

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