# EXPLAIN-IT: Towards Explainable AI for Unsupervised Network Traffic Analysis

arXiv: https://arxiv.org/abs/2003.01670 · HTML: https://mlsanity.com/paper/2003.01670/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 9 of 9)

- [DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications](https://mlsanity.com/paper/2109.11495/) (2021)
- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning](https://mlsanity.com/paper/2409.03632/) (2024)
- [Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges](https://mlsanity.com/paper/2112.04698/) (2021)
- [Deep Learning-driven Community Resilience Rating based on Intertwined Socio-Technical Systems Features](https://mlsanity.com/paper/2311.01661/) (2023)
- [KnAC: an approach for enhancing cluster analysis with background knowledge and explanations](https://mlsanity.com/paper/2112.08759/) (2021)
- [VitrAI -- Applying Explainable AI in the Real World](https://mlsanity.com/paper/2102.06518/) (2021)
- [Do you see what I see? An Ambiguous Optical Illusion Dataset exposing limitations of Explainable AI](https://mlsanity.com/paper/2505.21589/) (2025)
- [Uncertainty Awareness and Trust in Explainable AI- On Trust Calibration using Local and Global Explanations](https://mlsanity.com/paper/2509.08989/) (2025)

## Abstract (arXiv)

The application of unsupervised learning approaches, and in particular of clustering techniques, represents a powerful exploration means for the analysis of network measurements. Discovering underlying data characteristics, grouping similar measurements together, and identifying eventual patterns of interest are some of the applications which can be tackled through clustering. Being unsupervised, clustering does not always provide precise and clear insight into the produced output, especially when the input data structure and distribution are complex and difficult to grasp. In this paper we introduce EXPLAIN-IT, a methodology which deals with unlabeled data, creates meaningful clusters, and suggests an explanation to the clustering results for the end-user. EXPLAIN-IT relies on a novel explainable Artificial Intelligence (AI) approach, which allows to understand the reasons leading to a particular decision of a supervised learning-based model, additionally extending its application to the unsupervised learning domain. We apply EXPLAIN-IT to the problem of YouTube video quality classification under encrypted traffic scenarios, showing promising results.