Combination of quantum-based optimizer and feature pyramid network for intrusion detection in Cloud-IoT environments.

Rejab Hajlaoui, Mohamed Shalaby, Raed H C Alfilh, Narinderjit Singh Sawaran Singh

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Rejab HajlaouiDepartment of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il, Ha'il City, Saudi Arabia.

Mohamed ShalabyDepartment of Electrical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11432, Riyadh, Saudi Arabia.

Raed H C AlfilhDepartment of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq. raedalabedy@iunajaf.edu.iq.

Narinderjit Singh Sawaran SinghFaculty of Data Science and Information Technology, INTI International University, Persiaran Perdana BBN, Putra Nilai, 71800, Nilai, Malaysia.

Funding

Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) IMSIU-DDRSP2503

6 · The paper itself

Abstract

The fast-changing Cloud-IoT environment requires effective and dynamic intrusion detection systems (IDS). In order to overcome the weaknesses of the current solutions in managing high-dimensional, heterogeneous network traffic, this paper presents an improved structure with an Enhanced Feature Pyramid Network (EFPN) and a Quantum-Enhanced Child Drawing Development Optimizer (Q-CDDO). The EFPN has been scaled to tabular network data based on multi scale features extraction as pseudo-images. The quantum rotation gates are used in the Q-CDDO for better adjusting the hyperparameters in the EFPN structure with high-dimensional spaces. The model is validated on standard benchmark functions including the CIC-IDS-2017 and Bot-IoT datasets. Th results show an accuracy of 96.3% and 94.6% on the CIC-IDS-2017 and Bot-IoT datasets, respectively. The synergistic effect of both constituents, and the discriminative power of the model are verified by the studies of the ablation and the visualization, respectively. This paper contributes to the progress of IDS and proves the potential of quantum-inspired metaheuristics in cybersecurity.

Indexed as

Collaborative planningEnhanced feature pyramid networkInternetofThingsIntrusion detection systemsQuantum-enhanced child drawing development optimizer

Identifiers

PMID41639150

DOI10.1038/s41598-026-35242-w

PMCPMC12923893

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LicenceCC BY-NC-ND

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