Co-located with ACM CoNEXT 2026 · Utrecht, The Netherlands · December 2026

Network Traffic Classification – Reloaded

NTC-R 2026

Rigorous. Reproducible. Reloaded.

Network traffic classification is our flagship scenario for a broader question: How do we build trustworthy, reproducible AI for networking?

Why NTC-R?

A timely and necessary reassessment of network traffic classification.

Internet traffic classification has been a central topic in networking for more than two decades. It has evolved from traffic engineering, QoS, and application-aware management to encrypted traffic analysis, machine learning, and, more recently, AI-based approaches inspired by large language and foundation models.

Recent studies, however, have revealed major methodological weaknesses, particularly in representation learning for encrypted traffic. Reported improvements often arise from dataset leakage, shortcut learning, or unrealistic evaluation settings, raising doubts about whether models learn meaningful traffic properties or merely capture spurious correlations.

At the same time, the field still lacks widely accepted tasks, benchmarks, standardized datasets, and reproducible evaluation practices. Fragmented datasets, inconsistent pipelines, and limited reproducibility continue to slow scientific progress.

NTC-R 2026 brings together researchers from networking, machine learning, security, and systems to reassess the state of the art and help define a research agenda grounded in rigor, reproducibility, and realistic deployment assumptions.

Traffic classification is our flagship scenario, but the questions it raises - trustworthy evaluation, reproducibility, and robustness to shortcuts - cut across networking as a whole. We therefore warmly welcome contributions that carry lessons from adjacent problems in computer networks and, more broadly, from trustworthy AI for networking, wherever they sharpen how the community measures genuine progress.

Missing “ImageNet for NTC”

The research community still lacks a shared dataset and a common set of tasks on which different proposals can be fairly compared and tested. Without shared benchmarks, evaluation pipelines, and best practices, it remains difficult to distinguish genuine progress from results that exploit dataset artefacts or unrealistic assumptions.

Rather than focusing on incremental performance gains, NTC-R 2026 promotes a rigorous scientific discussion on evaluation methodology, benchmarking, reproducibility, trustworthy AI for traffic classification, and representation learning approaches grounded in networking knowledge.

Topics of Interest

The workshop solicits original research papers, measurement studies, system papers, position papers, reproducibility studies, benchmark contributions, and negative results centered on Network Traffic Classification - and, more broadly, on trustworthy and reproducible AI for networking. Topics include, but are not limited to:

Benchmarking methodologies and reproducibility
Open datasets and traffic traces
Analysis of biases in existing datasets
Detection and mitigation of shortcut learning
Encrypted traffic classification
Foundation models and LLM-inspired approaches for networking
Explainable and interpretable traffic classification
Lightweight and deployable ML solutions
Adversarial robustness and evasion resistance
Measurement studies and operational experience
Reproducibility reports and artifact evaluation
Negative results and lessons learned
Shortcut-robust approaches for network traffic classification
Lessons from trustworthy AI in adjacent networking problems
Evaluation and benchmarking across networking ML tasks

Important Dates

All deadlines are Anywhere on Earth (AoE).

14 July 2026
Paper Submission Deadline
30 August 2026
Acceptance Notification
25 September 2026
Camera-Ready Due
15 October 2026
Program Available Online
TBA — within 7–11 December 2026
Workshop Day
Co-located with ACM CoNEXT 2026, Utrecht, The Netherlands

Submission Instructions

NTC-R 2026 adopts a double-blind peer review process. Submissions on traffic classification are our core focus, and we equally welcome work from adjacent networking problems that advances trustworthy, reproducible AI for the field. Authors are strongly encouraged to release all supporting materials alongside their submissions.

Paper Format

  • At most five (5) pages, excluding references and appendix
  • Two-column, 10pt ACM format
  • Double-blind: make sure to preserve anonymity
  • LaTeX users: ACM conference proceedings template

Open Science Encouraged

  • Code and software artifacts
  • Datasets and traffic traces
  • Experimental artifacts
  • Reproducibility instructions

Releasing materials is not required for acceptance, but strongly encouraged in the spirit of reproducible research.

Authors of accepted papers are expected to present their work at the workshop. Accepted papers will appear in the ACM Digital Library.

Submissions are handled via HotCRP. Prepare your paper following the format above, then submit through the portal.
Submit on HotCRP →

Workshop Format

NTC-R 2026 is planned as a one-day event. The accepted-paper program will be published online on 15 October 2026.

Opening & Invited Keynote

Setting the scene: twenty years of network traffic classification, what went wrong, and where the field goes from here.

Technical Paper Sessions

Presentations of accepted regular, short, position, and reproducibility/benchmark papers.

Panel: “What Went Wrong in Traffic Classification Research?”

A structured debate on evaluation flaws, shortcut learning, dataset leakage, and methodological blind spots in the literature.

Benchmarking & Reproducibility Discussion

Community-wide conversation on open datasets, shared benchmarks, and reproducibility standards for traffic classification.

Closing: Future Research Directions

Open discussion on the next research agenda for rigorous, reproducible, and practically relevant network traffic classification.

The detailed schedule with accepted papers will be published online on 15 October 2026.
Artifact submissions, public dataset releases, and reproducibility challenges are explicitly encouraged.

Invited Speaker

To Be Announced

The keynote speaker will be announced once confirmed. NTC-R 2026 will feature an invited talk from a leading researcher working at the intersection of internet traffic analysis, machine learning for networking, and reproducible evaluation.

Organizers

Steering Committee
Marco Mellia
Politecnico di Torino, Italy
Dario Rossi
Huawei Technologies, France
Program Chairs
Matteo Boffa
Politecnico di Torino, Italy
Francesco Bronzino
ENS de Lyon, France
Paul Schmitt
Cal Poly, USA
Technical Program Committee
Aristide Tanyi-Jong Akem
University of Southampton, UK
Giovanni Apruzzese
Reykjavik University, Iceland
Levente Csikor
A*STAR, Singapore
Nick Feamster
University of Chicago, USA
Claudio Fiandrino
IMDEA Networks Institute, Spain
Alessandro Finamore
Huawei Technologies, France
Arpit Gupta
UC Santa Barbara, USA
Prankur Gupta
Meta, USA
Minzhao Lyu
University of New South Wales, Australia
Paul Patras
University of Edinburgh, UK
Valerio Persico
University of Naples Federico II, Italy
Adrien Schoen
CNRS, France
Walter Willinger
NIKSUN, USA

Utrecht, The Netherlands

NTC-R 2026 is co-located with ACM CoNEXT 2026, held in Utrecht, The Netherlands, 7–11 December 2026. The exact workshop day within that window is to be announced.

Utrecht is easily accessible from Amsterdam Schiphol Airport (30 min by train) and sits at the heart of the Dutch rail network.

For full venue details, accommodation options, and visa information, please refer to the main conference website.

How to Register

Workshop registration is handled through ACM CoNEXT 2026. Registering for the main conference grants access to all co-located workshops including NTC-R 2026. Registration will open via the CoNEXT 2026 website.

Get in Touch

For questions about submissions, the program, or the workshop in general, reach out to the Program Chairs.

Matteo Boffa
Program Chair
Politecnico di Torino, Italy
Francesco Bronzino
Program Chair
ENS de Lyon, France
Paul Schmitt
Program Chair
Cal Poly, USA