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

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.