Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks

Amit Kumar; Vivek Kumar; Abhay Bhadauria

doi:10.31127/tuje.1630410

Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks

Abstract

With the rapid advancement of network technology, attacks such as denial-of-service (DoS), distributed denial-of-service (DDoS), and unknown or emerging threats are becoming more complex and harder to detect using conventional methods. Traditional intrusion detection systems (IDS) often struggle to detect attacks in high-speed real-time networks due to their reliance on rule-based or signature-based detection methods and limited processing speed. Additionally, organizations and industries are facing significant challenges due to the expansion of electronic devices like the Internet of Things (IoT) and running computer applications. There is an urgent need to secure these sensitive IoT high-speed network traffic systems. To address these limitations, this study develops a reliable and effective Hybrid Machine Learning-based Real-time Intrusion Detection System (HMLRT-IDS) to detect DoS, DDoS, and emerging attacks in real-time network traffic. The study proposes a Python programming-based algorithm for feature extraction that sniffs data from real-time network traffic and extracts relevant data related to the features, enabling the successful identification of cyber threats in real-time network traffic. Moreover, the Real-time Network Intrusion Detection-23 (RTNID23) dataset is constructed to evaluate the proposed HMLRT-IDS. Experimental results demonstrate that HMLRT-IDS achieves a remarkable accuracy of 99.88% with a response time as low as 0.001 seconds, making it highly efficient for real-time applications. Additionally, the proposed system outperforms several existing methods in terms of accuracy, speed, and adaptability, offering a significant advancement in intrusion detection for high-speed IoT or edge computing networks. This model simplifies the analysis of attacks and aids in determining the appropriate response when an attack occurs.

Keywords

References

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Details

Primary Language

English

Subjects

Information Security Management

Journal Section

Research Article

Authors

Amit Kumar ^*
0000-0002-4619-079X
India

Vivek Kumar This is me
0000-0002-2863-3200
India

Abhay Bhadauria
India

Publication Date

July 1, 2025

Submission Date

January 31, 2025

Acceptance Date

May 11, 2025

Published in Issue

Year 2025 Volume: 9 Number: 3

DOI

https://doi.org/10.31127/tuje.1630410

IZ

https://izlik.org/JA69AD93FZ

Cite

RIS / Bibtex

APA

Kumar, A., Kumar, V., & Bhadauria, A. (2025). Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks. Turkish Journal of Engineering, 9(3), 600-611. https://doi.org/10.31127/tuje.1630410

AMA

1.Kumar A, Kumar V, Bhadauria A. Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks. TUJE. 2025;9(3):600-611. doi:10.31127/tuje.1630410

Chicago

Kumar, Amit, Vivek Kumar, and Abhay Bhadauria. 2025. “Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks”. Turkish Journal of Engineering 9 (3): 600-611. https://doi.org/10.31127/tuje.1630410.

EndNote

Kumar A, Kumar V, Bhadauria A (July 1, 2025) Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks. Turkish Journal of Engineering 9 3 600–611.

IEEE

[1]A. Kumar, V. Kumar, and A. Bhadauria, “Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks”, TUJE, vol. 9, no. 3, pp. 600–611, July 2025, doi: 10.31127/tuje.1630410.

ISNAD

Kumar, Amit - Kumar, Vivek - Bhadauria, Abhay. “Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks”. Turkish Journal of Engineering 9/3 (July 1, 2025): 600-611. https://doi.org/10.31127/tuje.1630410.

JAMA

1.Kumar A, Kumar V, Bhadauria A. Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks. TUJE. 2025;9:600–611.

MLA

Kumar, Amit, et al. “Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks”. Turkish Journal of Engineering, vol. 9, no. 3, July 2025, pp. 600-11, doi:10.31127/tuje.1630410.

Vancouver

1.Amit Kumar, Vivek Kumar, Abhay Bhadauria. Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks. TUJE. 2025 Jul. 1;9(3):600-11. doi:10.31127/tuje.1630410

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