Phishing Email Detection DistilBERT

What it is

This model uses the DistilBERT architecture to classify emails as phishing or legitimate, providing a powerful tool for enhancing cybersecurity measures.

This model offers an efficient and accurate solution for detecting phishing emails, leveraging the DistilBERT architecture to provide high performance with reduced computational requirements.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited flexibility for custom training datasets

   The model is pre-trained on a specific dataset and retraining requires significant expertise in NLP.

2. 02

   Over-reliance on English language data

   Performance may degrade when classifying emails in languages other than English due to the training dataset's composition.

3. 03

   Potential for false positives/negatives

   The model might misclassify legitimate emails as phishing or vice versa, leading to security vulnerabilities or user inconvenience.

4. 04

   Requires continuous updates to adapt to new phishing techniques

   Phishing tactics evolve rapidly; the model's accuracy could diminish if not updated regularly with new data.

What it does well

• High accuracy in detecting phishing emails using DistilBERT

• Efficient model size for faster inference times

• Pre-trained on a large dataset of email texts

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.