Malicious Url Detector

🚀 Malicious-Url-Detector

Leveraging this fine-tuned model, you can identify harmful links intended to exploit users, such as phishing or malware URLs, by accurately classifying them as either malicious or benign.

🚀 Quick Start

Below is a quick example showing how to use this model with the 🤗 Transformers pipeline:

from transformers import pipeline

# Initialize the text-classification pipeline with this fine-tuned model
classifier = pipeline(
    "text-classification",
    model="Eason918/malicious-url-detector",
    truncation=True
)

# Example URL
url = "http://example.com/suspicious-link"

# Classify the URL
result = classifier(url)
print(result)

✨ Features

• URL Classification: Detect whether a URL is malicious (e.g., phishing, malware) or benign.
• Security Pipelines: Integrate into email filtering systems or website scanning tools to flag harmful links.

📚 Documentation

Model Details

Model Description

This model is a fine-tuned version of distilbert/distilbert-base-uncased, adapted specifically for malicious URL detection. It employs a text-classification approach to distinguish between benign and malicious URLs. By learning patterns from a curated dataset of phishing, malware, and legitimate URLs, it aims to help users and organizations bolster their defenses against a range of cyber threats.

Intended Use

Direct Use

• URL Classification: Detect whether a URL is malicious (e.g., phishing, malware) or benign.
• Security Pipelines: Integrate into email filtering systems or website scanning tools to flag harmful links.

Out-of-Scope Use

• General text classification tasks not related to malicious URL detection.
• Tasks requiring more nuanced context beyond the URL string (e.g., domain reputation, real-time link behavior).