Fractional Hand Wing Optimization Algorithm-Based Attention Ensemble Long-Short Term Memory for Phishing Detection and Mitigation

Phishing is a significant threat in cyberspace that causes losses in businesses and for individuals, in which phishing attacks use social engineering methods, such as emails as well as SMS, to disguise prohibited URLs as legitimate ones to steal users’ confidential information. As a result, the loss caused by these attacks calls for an effective process for preventing phishing attacks. Consequently, to reduce the threat posed by phishing emails, which have been increasing at an alarming rate in recent years, more effective phishing detection is required to improve the classification performance. In this research, a Deep Learning (DL)-enabled model is invented for phishing detection. The most imperative purpose of the current research is to create a detection as well as a mitigation model for phishing detection in web pages using an incremental Learning based Attention Ensemble Long-Short-Term Memory (LSTM) based on optimization. The proposed fractional hand wing optimization (HaWO) focuses on adjusting the hyperparameters of LSTM, which is crucial to understanding the significance of this research. Incorporating Adaptive incremental learning enables the model to update its parameters incrementally, with dynamic changes in the data. In addition, utilizing the channel and positional attention with the ensemble LSTM boosts the model’s capacity to fully exploit the pertinent features during training, which results in accurate findings of phishing emails. DL is also used in the mitigation module to add the attacker to the black–list as well as halt further communication from phishing websites. The proposed phishing detection model achieves accuracy, sensitivity, specificity, and [Formula: see text]1-score values of 97.40%, 96.60%, 98.21%, and 95.90%, respectively, for 80% of training. Specifically, the developed model achieves the metric values of 96.90%, 96.46%, 97.36%, and 95.41%, respectively, for the 8 [Formula: see text]-fold validation.