Efficient Pattern-Based Analysis of Network Flows in IoT Systems

The exponential growth of IoT deployments introduces new challenges in real-time network security and behavioral monitoring. Existing Intrusion Detection Systems (IDS) and activity analysis tools often face significant limitations when scaling to high-volume, high-speed IoT data streams. This paper proposes a novel framework that leverages advanced data series indexing methods, building upon our previous work and ULISSE’s variable-length subsequence indexing, to enable scalable, real-time pattern matching for IoT network flow analysis. We detail algorithmic steps for in-memory indexing, multidimensional flow analysis, and adaptive similarity search to enhance detection accuracy and efficiency. An experimental roadmap for validation on realistic IoT datasets is presented.