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cssupport
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mobilebert-sql-injection-detect

Text Classification
Transformers
PyTorch
English
mobilebert
pretraining
Inference Endpoints
Model card Files Community
2
mobilebert-sql-injection-detect
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---
license: apache-2.0
language:
- en
pipeline_tag: text-classification
---
# Model Card for Model ID

<!-- Based on  https://huggingface.co/t5-small, model generates SQL from text given table list with "CREATE TABLE" statements. 
This is a very light weigh model and could be used in multiple analytical applications. -->

Based on  [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) (MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks). This model detects SQLInjection attacks in the input string (check How To Below). This is a very very light  model (100mb) and can be used for edge computing use cases. Used dataset from [Kaggle](www.kaggle.com) called [SQl_Injection](https://www.kaggle.com/datasets/sajid576/sql-injection-dataset).
**Please test the model before deploying into any environment**.
Contact us for more info: [email protected]
### Code Repo
Here is the code repo https://github.com/cssupport23/AI-Model---SQL-Injection-Attack-Detector

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** cssupport ([email protected])
- **Model type:** Language model
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model :** [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased)

### Model Sources 

<!-- Provide the basic links for the model. -->

Please refer [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) for Model Sources.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
import torch
from transformers import MobileBertTokenizer, MobileBertForSequenceClassification


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer = MobileBertTokenizer.from_pretrained('google/mobilebert-uncased')
model = MobileBertForSequenceClassification.from_pretrained('cssupport/mobilebert-sql-injection-detect')
model.to(device)
model.eval()

def predict(text):
    inputs = tokenizer(text, padding=False, truncation=True, return_tensors='pt', max_length=512)
    input_ids = inputs['input_ids'].to(device)
    attention_mask = inputs['attention_mask'].to(device)

    with torch.no_grad():
        outputs = model(input_ids=input_ids, attention_mask=attention_mask)

    logits = outputs.logits
    probabilities = torch.softmax(logits, dim=1)
    predicted_class = torch.argmax(probabilities, dim=1).item()
    return predicted_class, probabilities[0][predicted_class].item()


#text = "SELECT * FROM users WHERE username = 'admin' AND password = 'password';"
#text = "select * from users where username = 'admin' and password = 'password';"
#text = "SELECT * from USERS where id  =  '1' or @ @1  =  1 union select 1,version  (    )   -- 1'"
#text = "select * from data where id  =  '1'  or @"
text ="select * from users where id  =  1 or 1#\"?  =  1 or 1  =  1 -- 1"
predicted_class, confidence = predict(text)

if predicted_class > 0.7:
    print("Prediction: SQL Injection Detected")
else:
    print("Prediction: No SQL Injection Detected")
    
print(f"Confidence: {confidence:.2f}")
# OUTPUT
# Prediction: SQL Injection Detected
# Confidence: 1.00
```


## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

[More Information Needed]

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
Could used in application where natural language is to be converted into SQL queries. 
[More Information Needed]



### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.



## Technical Specifications 

### Model Architecture and Objective

[google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased)

### Compute Infrastructure



#### Hardware

one P6000 GPU

#### Software

Pytorch and HuggingFace