The Effect of Generating Synthetic Data in Smart City Network Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F25%3A50022318" target="_blank" >RIV/62690094:18450/25:50022318 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s42979-025-03673-3" target="_blank" >https://link.springer.com/article/10.1007/s42979-025-03673-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s42979-025-03673-3" target="_blank" >10.1007/s42979-025-03673-3</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The Effect of Generating Synthetic Data in Smart City Network Systems

Popis výsledku v původním jazyce

This study examines the effect of synthetic data generation for balancing class distributions on the performance of classification algorithms in smart city network systems. Contrary to the assumption that data balancing improves classification performance, the analysis reveals a more complex impact. Using three publicly available network traffic benchmark datasets and four different balancing techniques, the study evaluates the performance of five classifiers on 65 classification tasks. The findings indicate that, for smaller datasets, classifiers that achieved the highest accuracy on unbalanced data did not benefit from synthetic data generation for minority classes. Although neural network-based classifiers showed improved performance with balanced data, these improvements came at the cost of lower overall classification scores. For larger datasets, balancing through random oversampling of minority classes and undersampling of majority classes helped improve classification. However, these improvements were limited to precision, with no significant gains in recall. The study offers valuable insights into using synthetic data for intrusion detection, emphasizing the challenges of intricate dependencies in network traffic data for generative models. The results align with previous research showing mixed effects of data balancing on classifier performance, contributing to a broader understanding of the limited efficacy of synthetic data in real-world network contexts. This experimental study highlights the need for a systematic benchmarking framework for synthetic data research, ensuring consistency in data balancing and classification processes. This work contributes to the ongoing discourse on the intersection of machine learning and cybersecurity, emphasizing the critical role of data in developing resilient intrusion detection systems. © The Author(s) 2025.

Název v anglickém jazyce

The Effect of Generating Synthetic Data in Smart City Network Systems

Popis výsledku anglicky

This study examines the effect of synthetic data generation for balancing class distributions on the performance of classification algorithms in smart city network systems. Contrary to the assumption that data balancing improves classification performance, the analysis reveals a more complex impact. Using three publicly available network traffic benchmark datasets and four different balancing techniques, the study evaluates the performance of five classifiers on 65 classification tasks. The findings indicate that, for smaller datasets, classifiers that achieved the highest accuracy on unbalanced data did not benefit from synthetic data generation for minority classes. Although neural network-based classifiers showed improved performance with balanced data, these improvements came at the cost of lower overall classification scores. For larger datasets, balancing through random oversampling of minority classes and undersampling of majority classes helped improve classification. However, these improvements were limited to precision, with no significant gains in recall. The study offers valuable insights into using synthetic data for intrusion detection, emphasizing the challenges of intricate dependencies in network traffic data for generative models. The results align with previous research showing mixed effects of data balancing on classifier performance, contributing to a broader understanding of the limited efficacy of synthetic data in real-world network contexts. This experimental study highlights the need for a systematic benchmarking framework for synthetic data research, ensuring consistency in data balancing and classification processes. This work contributes to the ongoing discourse on the intersection of machine learning and cybersecurity, emphasizing the critical role of data in developing resilient intrusion detection systems. © The Author(s) 2025.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/VJ02010016" target="_blank" >VJ02010016: Využití umělé inteligence pro zajištění kybernetické bezpečnosti Smart City</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

SN Computer Science

ISSN

2662-995X

e-ISSN

2661-8907

Svazek periodika

6

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

18

Strana od-do

"Article number: 174"

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85219672490