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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.08964 |
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| _version_ | 1866912374478340096 |
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| author | Jaber, Majed Michel, Julien Boutry, Nicolas Parrend, Pierre |
| author_facet | Jaber, Majed Michel, Julien Boutry, Nicolas Parrend, Pierre |
| contents | The dramatic increase of complex, multi-step, and rapidly evolving attacks in dynamic networks involves advanced cyber-threat detectors. The GPML (Graph Processing for Machine Learning) library addresses this need by transforming raw network traffic traces into graph representations, enabling advanced insights into network behaviors. The library provides tools to detect anomalies in interaction and community shifts in dynamic networks. GPML supports community and spectral metrics extraction, enhancing both real-time detection and historical forensics analysis. This library supports modern cybersecurity challenges with a robust, graph-based approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08964 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPML: Graph Processing for Machine Learning Jaber, Majed Michel, Julien Boutry, Nicolas Parrend, Pierre Machine Learning Artificial Intelligence Cryptography and Security The dramatic increase of complex, multi-step, and rapidly evolving attacks in dynamic networks involves advanced cyber-threat detectors. The GPML (Graph Processing for Machine Learning) library addresses this need by transforming raw network traffic traces into graph representations, enabling advanced insights into network behaviors. The library provides tools to detect anomalies in interaction and community shifts in dynamic networks. GPML supports community and spectral metrics extraction, enhancing both real-time detection and historical forensics analysis. This library supports modern cybersecurity challenges with a robust, graph-based approach. |
| title | GPML: Graph Processing for Machine Learning |
| topic | Machine Learning Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2505.08964 |