A Hybrid Framework for Improved Weighted Quantum Particle Swarm Optimization and Fast Mask Recurrent CNN to Enhance Phishing-URL Prediction Performance

Phishing websites are cybercrimes that aim to collect confidential data, including bank card numbers, bank accounts, and credentials. To detect phishing sites, specialists must extract the elements of the websites and utilize third-party resources. One of the drawbacks of these methods is that identifying phishing characteristics takes a lot of effort and knowledge. Second, the recognition of phishing websites is delayed when third-party services are used. A novel detecting system Improved Weighted Quantum Particle Swarm Optimisation (IWQPSO) and Fast Mask Recurrent Convolutional Neural Network (FMRCNN) proposed to strengthen and empower the technique of identifying phishing URLs. The proposed model does not require the retrieval of target website content or the use of any third-party services. Phishing attacks continue to pose significant cyber-security threats, particularly through deceptive URLs. This study proposes a novel approach to enhance phishing-URL prediction using a hybrid methodology. The method integrates an IWQPSO-FMRCNN. The IWQPSO algorithm is leveraged to optimize the weights and parameters of the FMRCNN model, enhancing its performance in distinguishing between legitimate and phishing URLs. By combining the strengths of evolutionary optimization and deep learning, the proposed approach aims to achieve higher accuracy 99.3%, precision 94.6%, F1-Score 96.7%, Recall 98.2% and AUC score 97.9% in detecting phishing URLs compared to existing methods. Experimental results demonstrate the effectiveness of the proposed hybrid method in improving phishing-URL prediction performance, providing a promising avenue for enhancing cyber-security measures against online threats.