# Determinacc~ao Autom'atica de Limiar de Deteccc~ao de Ataques em Redes de Computadores Utilizando Autoencoders

arXiv: https://arxiv.org/abs/2506.14937 · HTML: https://mlsanity.com/paper/2506.14937/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 17 Jun 2025.

## Builds on (most cited first, 4 of 4)

- [A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines](https://mlsanity.com/paper/1801.01586/) (2018)
- [Theoretical Analysis of the k-Means Algorithm - A Survey](https://mlsanity.com/paper/1602.08254/) (2016)
- [Exploiting SNMP-MIB Data to Detect Network Anomalies using Machine Learning Techniques](https://mlsanity.com/paper/1809.02611/) (2018)
- [Detecting network anomalies using machine learning and SNMP-MIB dataset with IP group](https://mlsanity.com/paper/1906.00863/) (2019)

## Abstract (arXiv)

Currently, digital security mechanisms like Anomaly Detection Systems using Autoencoders (AE) show great potential for bypassing problems intrinsic to the data, such as data imbalance. Because AE use a non-trivial and nonstandardized separation threshold to classify the extracted reconstruction error, the definition of this threshold directly impacts the performance of the detection process. Thus, this work proposes the automatic definition of this threshold using some machine learning algorithms. For this, three algorithms were evaluated: the K-Nearst Neighbors, the K-Means and the Support Vector Machine.