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Learn how to automatically contextualize massive streams of IoT logs and alerts without relying on complex queries or heavy machine learning models in this DEF CON 33 conference talk. Discover a novel, LLM-free approach that uses lightweight, modular correlation logic to enrich logs, infer context, and group related events across sensors, devices, and cloud services. Explore how time, topology, and behavioral attributes can build causality sequences that explain what happened, where, and why without human-crafted rules or expensive AI inference. Master practical techniques for deploying contextualization pipelines in resource-constrained IoT environments, whether defending smart homes, industrial OT networks, or edge devices. Gain access to open-source tools and methodologies for extracting meaningful insights from noisy data streams, enabling faster and more effective detection and response in IoT security scenarios.
