Hybrid Quantum-AI Model for Robust Phishing Detection in Next-Generation Consumer Electronics

Phishing attacks that target consumer electronics are on the rise very quickly. The rise in this number is due to the fact that consumer electronics are now more connected than ever. Traditional ways of finding phishing attacks have trouble capturing complex feature interactions. In this context, this paper proposed a Hybrid Quantum-AI Model for Phishing Detection in Next-Generation Consumer Electronics. The proposed model combines a classical deep learning block with a 4-qubit parametric quantum circuit. This lets it get features from the input data that are both classical and quantum-enhanced. The quantum layer uses data-driven RY and RZ rotations with entanglement to create a lot of quantum features. The model gets a 94% accuracy, a 0.9357 F 1 score, and a 0.9829 AUC when it uses a UCI dataset. Also, the proposed model performs better than the previous quantum-based ones.