Combination of quantum-based optimizer and feature pyramid network for intrusion detection in Cloud-IoT environments
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an improved intrusion detection system (IDS) using an Enhanced Feature Pyramid Network (EFPN) and Quantum-Enhanced Child Drawing Development Optimizer (Q-CDDO) for Cloud-IoT security. The novel approach achieves high accuracy in detecting network intrusions.
Area Of Science
- Cybersecurity
- Network Intrusion Detection Systems (IDS)
- Cloud-IoT Security
Background
- The dynamic Cloud-IoT environment necessitates advanced intrusion detection systems (IDS).
- Existing solutions struggle with high-dimensional, heterogeneous network traffic management.
- Limitations in current IDS hinder effective threat detection in complex network infrastructures.
Purpose Of The Study
- To present an improved IDS structure for Cloud-IoT environments.
- To enhance the management of high-dimensional and heterogeneous network traffic.
- To leverage quantum-inspired optimization for superior IDS performance.
Main Methods
- Developed an Enhanced Feature Pyramid Network (EFPN) adapted for tabular network data using multi-scale feature extraction.
- Integrated a Quantum-Enhanced Child Drawing Development Optimizer (Q-CDDO) utilizing quantum rotation gates for hyperparameter tuning.
- Validated the model on benchmark datasets: CIC-IDS-2017 and Bot-IoT.
Main Results
- Achieved 96.3% accuracy on the CIC-IDS-2017 dataset.
- Achieved 94.6% accuracy on the Bot-IoT dataset.
- Ablation studies confirmed the synergistic effect of EFPN and Q-CDDO; visualization verified the model's discriminative power.
Conclusions
- The proposed EFPN and Q-CDDO model significantly advances intrusion detection capabilities in Cloud-IoT.
- Quantum-inspired metaheuristics demonstrate strong potential for enhancing cybersecurity solutions.
- The study validates the effectiveness of the integrated approach for dynamic network traffic analysis.
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