Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data
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 to...
| Authors | Igor Cherepanov, David Sessler, Alex Ulmer et al. |
|---|---|
| Primary category | cs.HC |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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