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| Autores principales: | , , |
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| Formato: | Recurso digital |
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Zenodo
2025
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| Acceso en línea: | https://doi.org/10.5281/zenodo.18787910 |
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| _version_ | 1866901812581236736 |
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| author | Tzagkarakis, Christos Palpanas, Themis Tzagkarakis, George |
| author_facet | Tzagkarakis, Christos Palpanas, Themis Tzagkarakis, George |
| contents | <p>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18787910 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Efficient Pattern-Based Analysis of Network Flows in IoT Systems Tzagkarakis, Christos Palpanas, Themis Tzagkarakis, George <p>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.</p> |
| title | Efficient Pattern-Based Analysis of Network Flows in IoT Systems |
| url | https://doi.org/10.5281/zenodo.18787910 |