AtEdO-BLGBM: A Hybrid Optimization and Boosting Framework for Scalable Intrusion Detection in Heterogeneous IoT Networks

Kapil Shrivastava; Manish Tiwari; Prasun Chakrabartı

doi:10.31127/tuje.1715511

AtEdO-BLGBM: A Hybrid Optimization and Boosting Framework for Scalable Intrusion Detection in Heterogeneous IoT Networks

Abstract

IIntrusion detection is a network security measure that regularly tracks the data flow and prevents malicious activities in Internet of Things (IoT) Networks. A significant rise in connected devices has led to an increase in threats across the IoT. Numerous traditional techniques have been introduced to achieve significant attack detection; however, they have resulted in certain limitations in terms of flexibility, scalability, and performance in heterogeneous infrastructures. To overcome these challenges, this research proposes an Attack-Evaded Optimization Ensemble Bi-directional Long Short-Term Memory Gradient-Boosting Machine (AtEdO-BLGBM) model, which effectively detects intrusions in complex and dynamic network environments. The AtEdO algorithm fine-tunes parameters with better convergence and improved global optima in intrusion detection. Moreover, the proposed model utilizes the AtEdO generative adversarial network to handle class labels, thereby balancing the imbalanced data distribution in heterogeneous networks. The combined BLGBM approach reduces computational costs and enhances the training process, thereby improving detection capability. Finally, the AtEdO-BLGBM model yields 96.78% accuracy, 96.71% negative predictive value and 97.03% positive predictive value, demonstrating its effectiveness in accurate and efficient intrusion detection. In future work, we aim to integrate transformer-based architectures and energy-efficient optimization techniques to enhance real-time detection.

Keywords

References

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Details

Primary Language

English

Subjects

Information Security Management, Data Communications

Journal Section

Research Article

Authors

Kapil Shrivastava ^*
0000-0002-8777-3721
India

Manish Tiwari
0000-0001-6619-3624
India

Prasun Chakrabartı
0000-0001-8062-4144
India

Publication Date

October 8, 2025

Submission Date

June 6, 2025

Acceptance Date

September 17, 2025

Published in Issue

Year 2025 Volume: 9 Number: 4

DOI

https://doi.org/10.31127/tuje.1715511

IZ

https://izlik.org/JA53PP49CE

Cite

RIS / Bibtex

APA

Shrivastava, K., Tiwari, M., & Chakrabartı, P. (2025). AtEdO-BLGBM: A Hybrid Optimization and Boosting Framework for Scalable Intrusion Detection in Heterogeneous IoT Networks. Turkish Journal of Engineering, 9(4), 738-753. https://doi.org/10.31127/tuje.1715511

AMA

1.Shrivastava K, Tiwari M, Chakrabartı P. AtEdO-BLGBM: A Hybrid Optimization and Boosting Framework for Scalable Intrusion Detection in Heterogeneous IoT Networks. TUJE. 2025;9(4):738-753. doi:10.31127/tuje.1715511

Chicago

Shrivastava, Kapil, Manish Tiwari, and Prasun Chakrabartı. 2025. “AtEdO-BLGBM: A Hybrid Optimization and Boosting Framework for Scalable Intrusion Detection in Heterogeneous IoT Networks”. Turkish Journal of Engineering 9 (4): 738-53. https://doi.org/10.31127/tuje.1715511.

EndNote

Shrivastava K, Tiwari M, Chakrabartı P (October 1, 2025) AtEdO-BLGBM: A Hybrid Optimization and Boosting Framework for Scalable Intrusion Detection in Heterogeneous IoT Networks. Turkish Journal of Engineering 9 4 738–753.

IEEE

[1]K. Shrivastava, M. Tiwari, and P. Chakrabartı, “AtEdO-BLGBM: A Hybrid Optimization and Boosting Framework for Scalable Intrusion Detection in Heterogeneous IoT Networks”, TUJE, vol. 9, no. 4, pp. 738–753, Oct. 2025, doi: 10.31127/tuje.1715511.

ISNAD

Shrivastava, Kapil - Tiwari, Manish - Chakrabartı, Prasun. “AtEdO-BLGBM: A Hybrid Optimization and Boosting Framework for Scalable Intrusion Detection in Heterogeneous IoT Networks”. Turkish Journal of Engineering 9/4 (October 1, 2025): 738-753. https://doi.org/10.31127/tuje.1715511.

JAMA

1.Shrivastava K, Tiwari M, Chakrabartı P. AtEdO-BLGBM: A Hybrid Optimization and Boosting Framework for Scalable Intrusion Detection in Heterogeneous IoT Networks. TUJE. 2025;9:738–753.

MLA

Shrivastava, Kapil, et al. “AtEdO-BLGBM: A Hybrid Optimization and Boosting Framework for Scalable Intrusion Detection in Heterogeneous IoT Networks”. Turkish Journal of Engineering, vol. 9, no. 4, Oct. 2025, pp. 738-53, doi:10.31127/tuje.1715511.

Vancouver

1.Kapil Shrivastava, Manish Tiwari, Prasun Chakrabartı. AtEdO-BLGBM: A Hybrid Optimization and Boosting Framework for Scalable Intrusion Detection in Heterogeneous IoT Networks. TUJE. 2025 Oct. 1;9(4):738-53. doi:10.31127/tuje.1715511