Tootfinder

The Sweet Danger of Sugar: Debunking Representation Learning for Encrypted Traffic Classification
Yuqi Zhao, Giovanni Dettori, Matteo Boffa, Luca Vassio, Marco Mellia
https://arxiv.org/abs/2507.16438

The Sweet Danger of Sugar: Debunking Representation Learning for Encrypted Traffic Classification
Recently we have witnessed the explosion of proposals that, inspired by Language Models like BERT, exploit Representation Learning models to create traffic representations. All of them promise astonishing performance in encrypted traffic classification (up to 98% accuracy). In this paper, with a networking expert mindset, we critically reassess their performance. Through extensive analysis, we demonstrate that the reported successes are heavily influenced by data preparation problems, which all…

This https://arxiv.org/abs/2403.16634 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMS_…

Symbolic and User-friendly Geometric Algebra Routines (SUGAR) for Computations in Matlab
Geometric algebra (GA) is a mathematical tool for geometric computing, providing a framework that allows a unified and compact approach to geometric relations which in other mathematical systems are typically described using different more complicated elements. This fact has led to an increasing adoption of GA in applied mathematics and engineering problems. However, the scarcity of symbolic implementations of GA and its inherent complexity, requiring a specific mathematical background, make it…