Develop a hybrid improved weighted quantum wolf optimization and fast mask recurrent convolutional neural network toenhance the performance of phishing malicious video detection

Phishing attacks have evolved to exploit video content, presenting new challenges for detection systems. A hybrid improved weighted quantum wolf optimization (IWQWO) algorithm with fast mask recurrent convolution neural network (FMRCNN) is proposed, enhancing the detection performance of malicious phishing videos. The IWQWO optimizes feature selection and model parameters, leveraging the principles of quantum computing and the hunting behavior of wolves to effectively explore the search space and avoid local optima. It improves the quality of the features used by the neural network, enhancing its detection capabilities. The FMRCNN is designed to process video data, integrating spatial and temporal feature extraction. Recurrent layers capture temporal dependencies, convolutional layers extract spatial features, and mask layers focus on the most relevant parts of the data, reducing noise and irrelevant information. The proposed IWQWO-FMRCNN model is capable of accurately identifying malicious phishing videos with high detection rates and low false positives. Extensive experiments on benchmark datasets demonstrate the superiority of our approach over existing methods in terms of accuracy, computational efficiency, and adaptability to new phishing techniques. This hybrid model offers a significant advancement in cybersecurity, providing an effective tool for early detection and prevention of phishing attacks, thereby safeguarding users and digital infrastructure.