ankush-003 / NoSQLi-Detector

DistilBERT-based NoSQL Injection Payload Detection Model

NoSQLi-Detector

DistilBERT-based NoSQL Injection Payload Detection Model

• This repo contains a fine-tuned DistilBERT model that can be used to detect NoSQL injection payloads. The model was trained on a dataset of known NoSQL injection payloads, and it can be used to classify new payloads as either malicious or benign.

• The model is implemented in Python, and it can be used with any NoSQL database. The repo also includes a Jupyter notebook that demonstrates how to use the model.

Model on 🤗

• Visit 🤗

Gradio App on 🤗Spaces

• 🤗Spaces
• Gradio App

API Documentation

Use the gradio_client Python library or the @gradio/client Javascript package to query the demo via API.

python

pip install gradio_client

Named Endpoints: api_name: /predict

from gradio_client import Client

client = Client("https://ankush-003-ankush-003-nosqli-identifier.hf.space/")
result = client.predict(
				"Howdy!",	# str  in 'Enter Username' Textbox component
				"Howdy!",	# str  in 'Enter Password' Textbox component
				"Malitious",	# str (Option from: ['Malitious', 'Benign']) in 'Expected' Dropdown component
				"Howdy!",	# str  in 'Enter Payload' Textbox component
				api_name="/predict"
)
print(result)

Js

npm i -D @gradio/client

Named Endpoints: api_name: /predict

import { client } from "@gradio/client";

const app = await client("https://ankush-003-ankush-003-nosqli-identifier.hf.space/");
const result = await app.predict("/predict", [		
				"Howdy!", // string  in 'Enter Username' Textbox component		
				"Howdy!", // string  in 'Enter Password' Textbox component		
				"Malitious", // string (Option from: ['Malitious', 'Benign']) in 'Expected' Dropdown component		
				"Howdy!", // string  in 'Enter Payload' Textbox component
	]);

console.log(result.data);

References

• Text Classification 🤗Docs