Hybrid CNN XGBoost intrusion detection approach tuned by modified sine cosine algorithm towards better cloud security

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F25%3A50023081" target="_blank" >RIV/62690094:18470/25:50023081 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.tandfonline.com/doi/epdf/10.1080/09540091.2025.2549581?needAccess=true" target="_blank" >https://www.tandfonline.com/doi/epdf/10.1080/09540091.2025.2549581?needAccess=true</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/09540091.2025.2549581" target="_blank" >10.1080/09540091.2025.2549581</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hybrid CNN XGBoost intrusion detection approach tuned by modified sine cosine algorithm towards better cloud security

Popis výsledku v původním jazyce

Cloud computing (CC) delivers processing power and data storage on demand. It is one of the most significant computer science technologies, contributing to healthcare, industry, and the Internet of Things. One of CC&apos;s biggest security concerns are intrusion detection and separating harmful from legitimate communication, similar to computer networks. Although a wide range of intrusion detection systems is available today, they often suffer from misclassification issues, where the system can fail to recognize an attack as a threat or to mark normal traffic as malicious. This research proposes classifying network traffic using a convolutional neural network and extreme gradient boosting model. Additionally, a modified sine cosine algorithm is used to tune model hyperparameters for optimal performance. The presented framework was tested on major real-world TON IoT intrusion detection datasets. The proposed optimizer is compared to many recent metaheuristics in a matched experimental setting. The simulation results show that the suggested technique is superior to other methods for both datasets, with the best-performing optimized models achieving an accuracy of 96.667 on Windows 10 and 98.6731 on Windows 7 simulation.

Název v anglickém jazyce

Hybrid CNN XGBoost intrusion detection approach tuned by modified sine cosine algorithm towards better cloud security

Popis výsledku anglicky

Cloud computing (CC) delivers processing power and data storage on demand. It is one of the most significant computer science technologies, contributing to healthcare, industry, and the Internet of Things. One of CC&apos;s biggest security concerns are intrusion detection and separating harmful from legitimate communication, similar to computer networks. Although a wide range of intrusion detection systems is available today, they often suffer from misclassification issues, where the system can fail to recognize an attack as a threat or to mark normal traffic as malicious. This research proposes classifying network traffic using a convolutional neural network and extreme gradient boosting model. Additionally, a modified sine cosine algorithm is used to tune model hyperparameters for optimal performance. The presented framework was tested on major real-world TON IoT intrusion detection datasets. The proposed optimizer is compared to many recent metaheuristics in a matched experimental setting. The simulation results show that the suggested technique is superior to other methods for both datasets, with the best-performing optimized models achieving an accuracy of 96.667 on Windows 10 and 98.6731 on Windows 7 simulation.

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

CONNECTION SCIENCE

ISSN

0954-0091

e-ISSN

1360-0494

Svazek periodika

37

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

26

Strana od-do

"Article Number: 2549581"

Kód UT WoS článku

001574028700001

EID výsledku v databázi Scopus

2-s2.0-105016615324