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Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data — arXiv:2608.13575

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.13575
License (as archived): CC0-1.0
Archived: 2026-10-19T02:20:56.971Z
SHA-256 of the archived text: 9a231ab3b2e2d109d7fd5519a4ddfac9b5390a95ae013869d5ab320cda3dcfa4
Stable URL of this page: https://metraipolis.com/documento/dc_9cfd50efcbca

How to cite

Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data — arXiv:2608.13575 — arXiv (en), https://arxiv.org/abs/2608.13575 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_9cfd50efcbca (Metraipolis archive dc_9cfd50efcbca, sha256 9a231ab3b2e2d109d7fd5519a4ddfac9b5390a95ae013869d5ab320cda3dcfa4)

BibTeX

@misc{dc9cfd50efcbca,
  title = {Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data — arXiv:2608.13575},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.13575},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_9cfd50efcbca; sha256 9a231ab3b2e2d109d7fd5519a4ddfac9b5390a95ae013869d5ab320cda3dcfa4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.13575. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recent machine learning (ML) advances have demonstrated that deep learning (DL) achieves impressive results in different application domains, including the classification of computer network traffic to corresponding applications. However, the data frequently contains diverging patterns within a single predicted class. This presents a significant challenge to the ability to provide a clear and comprehensive explanation and emphasizes the necessity for tools capable of detecting and analyzing these patterns. Furthermore, the capacity to extract descriptive rules for classes is a crucial requirement in network traffic analysis and intrusion detection, particularly when leveraging advanced tools like next-generation firewalls. We provide a visual-interactive system that explains predictions of classes for network traffic. Global explanations derived from multiple samples of a given class contribute to understanding model predictions. Visualization of global explanations enables recognition of different patterns that offer experts a more comprehensive overview of its characteristics. We introduce a prototype that facilitates visual exploration and refinement of global explanations, enabling network experts to detect and refine new patterns for specific applications. These explanations support the identification of misleading features and the formulation of new rules for the management of networks. Our approach also aims at enabling ML experts to acquire new insights, including the possibility of separating or merging classes and the development of more accurate and reliable DL models. Our proposed prototype was evaluated by experts in machine learning and network analysis.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-17 in cs.AI).
Categorie arXiv: Human-Computer Interaction (cs.HC) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI).
Pagina del lavoro: https://arxiv.org/abs/2608.13575 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T02:02:02.841Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).