A Hybrid Transformer Ensemble Approach for Phishing Website Detection

The internet has developed into a veritable informational gold mine in the era of the digital age, and URLs (Uniform Resource Locator) act as access points to enormous volumes of material. For a variety of applications, including web content classification, fraud detection, and information retrieval, it is essential to extract useful properties from URLs. The proposed approach investigates the potential for extracting useful information from URLs using transformer-based models. Transformers can be configured to decode the underlying patterns and semantics in URLs, which frequently display complicated structures and variations. Due to their exceptional abilities in natural language processing, they are used to effectively encode and represent the properties of the provided URLs. The approach uses pre-trained transformer models like BERT (Bidirectional Encoder Representation from Transformers), optimizing their feature extraction performance on unique URLs. Later, a stacking strategy is used that combines machine learning models to further improve accuracy. Stacking is a potent ensemble approach and makes use of the diverse strengths of various models to provide predictions that are more reliable and accurate. By providing a quick and accurate method to identify phishing websites, the proposed approach makes contribution to field of internet security and protects consumers from potential cyber risks.