BERT FINETUNED ON PHISHING DETECTION This model is a fine tuned version of bert large uncased on an phishing dataset, capable of detecting phishing in its four most common forms: URLs, Emails, SMS messages and even websites. It achieves the following results on the evaluation set: Loss: 0.1953 Accuracy: 0.9717 Precision: 0.9658 Recall: 0.9670 False Positive Rate: 0.0249 Model description BERT is a transformers model pretrained on a large corpus of English data in a self supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. This model has the following configuration: 24 layer 1024 hidden dimension 16 attention heads 336M parameters Motivation and Purpose Phishing is one of the most frequent and most expensive cyber attacks according to several security reports. This model aims to efficiently and accurately prevent phishing attacks against individuals and organizations. To achieve it, BERT was trained on a diverse and robust dataset containing: URLs, SMS Messages, Emails and Websites, which allow…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy