Deep learning based phishing website detection

N. Subhashini, Amogh Banerjee, Abhi Kumar, S. Muthulakshmi, S. Revathi


Phishing attacks use fraudulent websites that trick people into disclosing sensitive information. More effective and precise methods are required to identify phishing websites so that people and organisations can be protected from the damaging effects of these online threats. The aim of this work is to develop a model that can identify phishing uniform resource locator (URLs) more accurately than current approaches while requiring less training time, testing time, and storage space. This research work proposes a novel method for identifying phishing websites using a long short-term memory (LSTM) gated recurrent unit (GRU) algorithm to detect phishing URLs. The accuracy of the suggested method is 98.89%, which is significantly better than the findings of earlier studies. The model also showed a need for shorter training and testing time, and a reduced amount of storage space.


deep learning; detection online security; long short-term memory; gated recurrent unit; phishing website;

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