Metaheuristics with deep learning driven phishing detection for sustainable and secure environment

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 su-periority of the proposed model with an improved accuracy of 95.78%.