Optimizing Network Intrusion Detection Performance with GNN-Based Feature Selection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13510%2F25%3A43899388" target="_blank" >RIV/44555601:13510/25:43899388 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.techscience.com/cmc/v85n2/63790" target="_blank" >https://www.techscience.com/cmc/v85n2/63790</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/cmc.2025.065885" target="_blank" >10.32604/cmc.2025.065885</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimizing Network Intrusion Detection Performance with GNN-Based Feature Selection

Popis výsledku v původním jazyce

The rapid evolution of AI-driven cybersecurity solutions has led to increasingly complex network infrastructures, which in turn increases their exposure to sophisticated threats. This study proposes a Graph Neural Network (GNN)-based feature selection strategy specifically tailored for Network Intrusion Detection Systems (NIDS). By modeling feature correlations and leveraging their topological relationships, this method addresses challenges such as feature redundancy and class imbalance. Experimental analysis using the KDDTest+ dataset demonstrates that the proposed model achieves 98.5% detection accuracy, showing notable gains in both computational efficiency and minority class detection. Compared to conventional machine learning methods, the GNN-based approach exhibits a superior capability to adapt to the dynamics of evolving cyber threats. The findings support the feasibility of deploying GNNs for scalable, real-time anomaly detection in modern networks. Furthermore, key predictive features, notably f35 and f23, are identified and validated through correlation analysis, thereby enhancing the model&apos;s interpretability and effectiveness.

Název v anglickém jazyce

Optimizing Network Intrusion Detection Performance with GNN-Based Feature Selection

Popis výsledku anglicky

The rapid evolution of AI-driven cybersecurity solutions has led to increasingly complex network infrastructures, which in turn increases their exposure to sophisticated threats. This study proposes a Graph Neural Network (GNN)-based feature selection strategy specifically tailored for Network Intrusion Detection Systems (NIDS). By modeling feature correlations and leveraging their topological relationships, this method addresses challenges such as feature redundancy and class imbalance. Experimental analysis using the KDDTest+ dataset demonstrates that the proposed model achieves 98.5% detection accuracy, showing notable gains in both computational efficiency and minority class detection. Compared to conventional machine learning methods, the GNN-based approach exhibits a superior capability to adapt to the dynamics of evolving cyber threats. The findings support the feasibility of deploying GNNs for scalable, real-time anomaly detection in modern networks. Furthermore, key predictive features, notably f35 and f23, are identified and validated through correlation analysis, thereby enhancing the model&apos;s interpretability and effectiveness.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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 periodika

Computers Materials &amp; Continua

ISSN

1546-2218

e-ISSN

1546-2226

Svazek periodika

85

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

2985-2997

Kód UT WoS článku

001592176100001

EID výsledku v databázi Scopus

2-s2.0-105017248169