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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-classification |
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--- |
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# Model Card for Model ID |
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<!-- Based on https://huggingface.co/t5-small, model generates SQL from text given table list with "CREATE TABLE" statements. |
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This is a very light weigh model and could be used in multiple analytical applications. --> |
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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). |
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**Please test the model before deploying into any environment**. |
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Contact us for more info: support@cloudsummary.com |
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### Code Repo |
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Here is the code repo https://github.com/cssupport23/AI-Model---SQL-Injection-Attack-Detector |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** cssupport (support@cloudsummary.com) |
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- **Model type:** Language model |
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- **Language(s) (NLP):** English |
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- **License:** Apache 2.0 |
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- **Finetuned from model :** [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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Please refer [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) for Model Sources. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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import torch |
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from transformers import MobileBertTokenizer, MobileBertForSequenceClassification |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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tokenizer = MobileBertTokenizer.from_pretrained('google/mobilebert-uncased') |
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model = MobileBertForSequenceClassification.from_pretrained('cssupport/mobilebert-sql-injection-detect') |
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model.to(device) |
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model.eval() |
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def predict(text): |
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inputs = tokenizer(text, padding=False, truncation=True, return_tensors='pt', max_length=512) |
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input_ids = inputs['input_ids'].to(device) |
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attention_mask = inputs['attention_mask'].to(device) |
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with torch.no_grad(): |
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outputs = model(input_ids=input_ids, attention_mask=attention_mask) |
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logits = outputs.logits |
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probabilities = torch.softmax(logits, dim=1) |
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predicted_class = torch.argmax(probabilities, dim=1).item() |
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return predicted_class, probabilities[0][predicted_class].item() |
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#text = "SELECT * FROM users WHERE username = 'admin' AND password = 'password';" |
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#text = "select * from users where username = 'admin' and password = 'password';" |
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#text = "SELECT * from USERS where id = '1' or @ @1 = 1 union select 1,version ( ) -- 1'" |
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#text = "select * from data where id = '1' or @" |
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text ="select * from users where id = 1 or 1#\"? = 1 or 1 = 1 -- 1" |
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predicted_class, confidence = predict(text) |
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if predicted_class > 0.7: |
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print("Prediction: SQL Injection Detected") |
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else: |
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print("Prediction: No SQL Injection Detected") |
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print(f"Confidence: {confidence:.2f}") |
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# OUTPUT |
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# Prediction: SQL Injection Detected |
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# Confidence: 1.00 |
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``` |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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[More Information Needed] |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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Could used in application where natural language is to be converted into SQL queries. |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## Technical Specifications |
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### Model Architecture and Objective |
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[google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) |
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### Compute Infrastructure |
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#### Hardware |
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one P6000 GPU |
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#### Software |
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Pytorch and HuggingFace |
