Abstract
Information technologies have intervened in every aspect of human life. This growth of connectivity, however, has radically changed the phishing attack landscape. In a phishing attack, users are tricked into providing data they would not willingly share otherwise. This attack is a persistent threat to the sustainability and security of ubiquitous systems. Hence, this paper introduces a novel metaheuristics deep learning-oriented phishing detection (MDLPD-SSE) technique for a sustainable and secure environment. The presented MDLPD-SSE model majorly focuses on identifying phishing websites. For this, the MDLPD-SSE method pre-processes the input URL to transform it into a compatible format. In addition, an improved simulated annealing-based feature selection (ISA-FS) approach was used to derive feature subsets. Furthermore, the long short-term memory (LSTM) model is utilized in this study to identify phishing. Finally, the bald eagle search (BES) optimization methodology was exploited to fine-tune the hyperparameters relevant to the LSTM model. Our outcomes demonstrated the superiority of the proposed model with an improved accuracy of 95.78%.