# Phishing URL Detection using Bi-LSTM

arXiv: https://arxiv.org/abs/2504.21049 · HTML: https://mlsanity.com/paper/2504.21049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [PhishGuard: A Convolutional Neural Network Based Model for Detecting Phishing URLs with Explainability Analysis](https://mlsanity.com/paper/2404.17960/) (2024)
- [Enhancing Phishing Detection through Feature Importance Analysis and Explainable AI: A Comparative Study of CatBoost, XGBoost, and EBM Models](https://mlsanity.com/paper/2411.06860/) (2024)
- [A Sophisticated Framework for the Accurate Detection of Phishing Websites](https://mlsanity.com/paper/2403.09735/) (2024)

## Abstract (arXiv)

Phishing attacks threaten online users, often leading to data breaches, financial losses, and identity theft. Traditional phishing detection systems struggle with high false positive rates and are usually limited by the types of attacks they can identify. This paper proposes a deep learning-based approach using a Bidirectional Long Short-Term Memory (Bi-LSTM) network to classify URLs into four categories: benign, phishing, defacement, and malware. The model leverages sequential URL data and captures contextual information, improving the accuracy of phishing detection. Experimental results on a dataset comprising over 650,000 URLs demonstrate the model's effectiveness, achieving 97% accuracy and significant improvements over traditional techniques.