Elevating intrusion detection and security fortification in intelligent networks through cutting-edge machine learning paradigms

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10259123" target="_blank" >RIV/61989100:27240/25:10259123 - isvavai.cz</a>

Result on the web

<a href="https://www.nature.com/articles/s41598-025-23754-w" target="_blank" >https://www.nature.com/articles/s41598-025-23754-w</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-025-23754-w" target="_blank" >10.1038/s41598-025-23754-w</a>

Result language

angličtina

Original language name

Elevating intrusion detection and security fortification in intelligent networks through cutting-edge machine learning paradigms

Original language description

The proliferation of IoT devices and their reliance on Wi-Fi networks have introduced significant security vulnerabilities, particularly the KRACK and Kr00k attacks, which exploit weaknesses in WPA2 encryption to intercept and manipulate sensitive data. Traditional intrusion detection systems (IDS) using classifiers face challenges such as model overfitting, incomplete feature extraction, and high false positive rates, limiting their effectiveness in real-world deployments. To address these challenges, this study proposes a robust multiclass machine learning based intrusion detection framework. The methodology integrates advanced feature selection techniques to identify critical attributes, mitigating redundancy and enhancing detection accuracy. Two distinct ML architectures are implemented: a baseline classifier pipeline and a stacked ensemble model combining noise injection, principal component analysis (PCA), and meta learning to improve generalization and reduce false positives. Evaluated on the AWID3 dataset, the proposed ensemble architecture achieves superior performance, with an accuracy of 98%, precision of 98%, recall of 98%, and a false positive rate of just 2%, outperforming existing state-of-the-art methods. This work demonstrates the efficacy of combining preprocessing strategies with ensemble learning to fortify network security against sophisticated Wi-Fi attacks, offering a scalable and reliable solution for IoT environments. Future directions include real-time deployment and adversarial resilience testing to further enhance the model&apos;s adaptability.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

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

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Volume of the periodical

15

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

18

Pages from-to

nestránkováno

UT code for WoS article

001616350200011

EID of the result in the Scopus database

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