Phishing detection and zero trust security using advanced neural architectures and noise injection

Abstract

Phishing attacks have developed very complex making the use of cultured detection technologies significant for the protection of sensitive information. The research presents a new framework for detecting phishing founded on the convolutional LSTM (ConvLSTM) network and neural ordinary differential equations (Neural ODEs) thereby refining detection robustness and accuracy. It joins a number of advanced methods like Gaussian noise injection for enhancing the simplification ability of the model and a hybrid Particle Swarm Optimization (PSO) and Sailfish Optimization (SFO) method for effective feature selection. In addition, it confirms safe decisions and regularly reviewing phishing risks by means of a genetic algorithm to improve the Zero Trust Implementation Framework. The optional model performs very well with F1 scores above 99 and recall, accuracy, and precision above 99. Associated to existing methods the proposed model performs significantly better reducing false positives and false negatives and contribute a higher degree of phishing detection accuracy. Future developments to the system will contain multi-modal data integration actual flexibility intelligible AI, and more scalability to handle important applications. Increasing the dataset to include changing phishing techniques could further improve detection services and support the system’s resistance to new threats.