Fine-Tuning BERT for Phishing URL Detection: A Beginner’s Guide

Author(s): Anoop Maurya

Originally published on Towards AI.

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In the realm of artificial intelligence, the emergence of transformer models has revolutionized natural language processing (NLP). While large models with billions of parameters dominate the landscape, smaller models can still deliver impressive results. In this guide, we will explore how to fine-tune BERT, a model with 110 million parameters, specifically for the task of phishing URL detection. We will cover essential concepts and provide a comprehensive example using Python code.

Phishing is a form of cybercrime where attackers impersonate legitimate entities to deceive individuals into revealing sensitive information, such as usernames, passwords, or credit card details. Phishing URLs are often the first point of contact in these scams, making their detection crucial for cybersecurity.Detecting phishing URLs can significantly reduce the risk of falling victim to these attacks. Machine learning models, particularly those based on deep learning architectures like BERT, have shown great promise in identifying malicious URLs by analyzing their textual features.

BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based model designed to understand the context of words in a sentence. Unlike traditional models that read text sequentially, BERT processes text bidirectionally, allowing it… Read the full blog for free on Medium.

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Published via Towards AI