Malicious-URL-Detector-v2

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.

Model Details

Model Description

This model is a fine-tuned version of distilroberta-base, 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, the model helps users and organizations enhance their defenses against a wide range of cyber threats.

• Developed by: Eason Liu
• Language: English
• Model Type: Text Classification (URL-focused)
• Finetuned From: distilroberta-base

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: This model is specialized for URL data and may not perform well on arbitrary text inputs.
• Advanced Contextual Analysis: It does not consider broader context such as domain reputation or real-time link behavior.

How to Get Started

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-v2",
    truncation=True
)

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

# Get the classification result
result = classifier(url)
print(result)
# Example output: [{'label': 'malicious', 'score': 0.9876}]