Evaluating Feature Encodings for Unsupervised Machine Learning Classification in Automotive Ethernet Network
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Evaluating Feature Encodings for Unsupervised Machine Learning Classification in Automotive Ethernet Network
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 12th International Conference on Soft Computing & Machine Intelligence (ISCMI)
ISBN
979-8-3315-8691-1
ISSN
2640-0154
e-ISSN
2640-0146
Number of pages
7
Pages from-to
15-21
Publisher name
Institute of Electrical and Electronics Engineers, Inc.
Place of publication
Rio de Janeiro
Event location
Rio de Janeiro
Event date
Nov 21, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001699548500004