Evaluating Feature Encodings for Unsupervised Machine Learning Classification in Automotive Ethernet Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43978389" target="_blank" >RIV/49777513:23520/25:43978389 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11358553" target="_blank" >https://ieeexplore.ieee.org/document/11358553</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ISCMI67495.2025.11358553" target="_blank" >10.1109/ISCMI67495.2025.11358553</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluating Feature Encodings for Unsupervised Machine Learning Classification in Automotive Ethernet Network

Popis výsledku v původním jazyce

Categorical attributes such as MAC and IP addresses constitute an integral part of Ethernet network data, and play a crucial role in modern network infrastructure. Representing these intrinsic entities with high cardinality presents a considerable performance challenge pertaining to machine learning tasks. In order to better manage the representations of the categorical attributes found in network data, this work presents new methods for transforming them. Some of these encoding schemes are designed using domain knowledge to limit the number of dimensions introduced in data while performing transformations. This study uses two specific Autoencoder deep neural networks for the unsupervised classification task to help assess the classification performance for the proposed encoding schemes. These varied encodings used to transform Ethernet network data from a real vehicle serve as a novel contribution to the feature engineering for analyzing the network data using machine learning approaches. The evaluation results show that the proposed techniques have a key impact on the classification performance, and the encoding schemes IE and ISF performed reasonably well in all three attack scenarios for each model.

Název v anglickém jazyce

Evaluating Feature Encodings for Unsupervised Machine Learning Classification in Automotive Ethernet Network

Popis výsledku anglicky

Categorical attributes such as MAC and IP addresses constitute an integral part of Ethernet network data, and play a crucial role in modern network infrastructure. Representing these intrinsic entities with high cardinality presents a considerable performance challenge pertaining to machine learning tasks. In order to better manage the representations of the categorical attributes found in network data, this work presents new methods for transforming them. Some of these encoding schemes are designed using domain knowledge to limit the number of dimensions introduced in data while performing transformations. This study uses two specific Autoencoder deep neural networks for the unsupervised classification task to help assess the classification performance for the proposed encoding schemes. These varied encodings used to transform Ethernet network data from a real vehicle serve as a novel contribution to the feature engineering for analyzing the network data using machine learning approaches. The evaluation results show that the proposed techniques have a key impact on the classification performance, and the encoding schemes IE and ISF performed reasonably well in all three attack scenarios for each model.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2025 12th International Conference on Soft Computing &amp; Machine Intelligence (ISCMI)

ISBN

979-8-3315-8691-1

ISSN

2640-0154

e-ISSN

2640-0146

Počet stran výsledku

7

Strana od-do

15-21

Název nakladatele

Institute of Electrical and Electronics Engineers, Inc.

Místo vydání

Rio de Janeiro

Místo konání akce

Rio de Janeiro

Datum konání akce

21. 11. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001699548500004