A distilBERT based SQL Injection Detection Model
Model description
This model, based on DistilBERT, is specifically tailored for the detection of SQL injection attacks. Through fine-tuning using the Hugging Face's Trainer API, the model has been trained to identify potentially malicious SQL queries with high accuracy.
- Architecture: DistilBERT
- Fine-tuning Method: Trainer API
- Performance Metrics:
- F1-score: 99.86%
- Accuracy: 99.99%
- Training Epochs: 6
Dataset description
The model was fine-tuned on the SQL Injectiom dataset, curated and made available by SAJID576 on Kaggle. This dataset comprises of 30,920 rows of SQL queries, including both benign and malicious examples, providing a comprehensive training corpus for robust model development.
- Dataset Source: https://www.kaggle.com/datasets/sajid576/sql-injection-dataset/data
- Size: 30,920 rows
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