# Visualization Of Class Activation Maps To Explain AI Classification Of Network Packet Captures

arXiv: https://arxiv.org/abs/2209.02045 · HTML: https://mlsanity.com/paper/2209.02045/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 5 Sep 2022.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning](https://mlsanity.com/paper/1709.02656/) (2017)
- [A Survey on Big Data for Network Traffic Monitoring and Analysis](https://mlsanity.com/paper/2003.01648/) (2020)

## Built on by (most cited first, 3 of 3)

- [Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Features](https://mlsanity.com/paper/2308.00710/) (2023)
- [Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data](https://mlsanity.com/paper/2608.13575/) (2026)
- [Beyond Measurement Metrics: A Human-Centered Framework for Semantic Validation of Network Traffic Classification](https://mlsanity.com/paper/2609.17014/) (2026)

## Abstract (arXiv)

The classification of internet traffic has become increasingly important due to the rapid growth of today's networks and applications. The number of connections and the addition of new applications in our networks causes a vast amount of log data and complicates the search for common patterns by experts. Finding such patterns among specific classes of applications is necessary to fulfill various requirements in network analytics. Deep learning methods provide both feature extraction and classification from data in a single system. However, these networks are very complex and are used as black-box models, which weakens the experts' trust in the classifications. Moreover, by using them as a black-box, new knowledge cannot be obtained from the model predictions despite their excellent performance. Therefore, the explainability of the classifications is crucial. Besides increasing trust, the explanation can be used for model evaluation gaining new insights from the data and improving the model. In this paper, we present a visual interactive tool that combines the classification of network data with an explanation technique to form an interface between experts, algorithms, and data.