# Open-Source Framework for Encrypted Internet and Malicious Traffic Classification

arXiv: https://arxiv.org/abs/2206.10144 · HTML: https://mlsanity.com/paper/2206.10144/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 57 references that are in the map.
- First posted on arXiv 21 Jun 2022.

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

- [Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection](https://mlsanity.com/paper/1704.08996/) (2017)
- [EvalAI: Towards Better Evaluation Systems for AI Agents](https://mlsanity.com/paper/1902.03570/) (2019)
- [Deep Learning for Encrypted Traffic Classification: An Overview](https://mlsanity.com/paper/1810.07906/) (2018)
- [New Directions in Automated Traffic Analysis](https://mlsanity.com/paper/2008.02695/) (2020)
- [How to Achieve High Classification Accuracy with Just a Few Labels: A Semi-supervised Approach Using Sampled Packets](https://mlsanity.com/paper/1812.09761/) (2018)
- [Robust Smartphone App Identification Via Encrypted Network Traffic Analysis](https://mlsanity.com/paper/1704.06099/) (2017)
- [DeepMAL -- Deep Learning Models for Malware Traffic Detection and Classification](https://mlsanity.com/paper/2003.04079/) (2020)
- [Large-scale Mobile App Identification Using Deep Learning](https://mlsanity.com/paper/1910.02350/) (2019)
- [NetML: A Challenge for Network Traffic Analytics](https://mlsanity.com/paper/2004.13006/) (2020)
- [Robust Machine Learning for Encrypted Traffic Classification](https://mlsanity.com/paper/1603.04865/) (2016)
- [Multi-Task Hierarchical Learning Based Network Traffic Analytics](https://mlsanity.com/paper/2106.03850/) (2021)

## Built on by (most cited first, 1 of 1)

- [Multi-View Fusion for Encrypted C2 Detection: A Leakage-Controlled Measurement Study of Evaluation Pitfalls](https://mlsanity.com/paper/2609.27311/) (2026)

## Abstract (arXiv)

Internet traffic classification plays a key role in network visibility, Quality of Services (QoS), intrusion detection, Quality of Experience (QoE) and traffic-trend analyses. In order to improve privacy, integrity, confidentiality, and protocol obfuscation, the current traffic is based on encryption protocols, e.g., SSL/TLS. With the increased use of Machine-Learning (ML) and Deep-Learning (DL) models in the literature, comparison between different models and methods has become cumbersome and difficult due to a lack of a standardized framework. In this paper, we propose an open-source framework, named OSF-EIMTC, which can provide the full pipeline of the learning process. From the well-known datasets to extracting new and well-known features, it provides implementations of well-known ML and DL models (from the traffic classification literature) as well as evaluations. Such a framework can facilitate research in traffic classification domains, so that it will be more repeatable, reproducible, easier to execute, and will allow a more accurate comparison of well-known and novel features and models. As part of our framework evaluation, we demonstrate a variety of cases where the framework can be of use, utilizing multiple datasets, models, and feature sets. We show analyses of publicly available datasets and invite the community to participate in our open challenges using the OSF-EIMTC.