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--- |
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license: mit |
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library_name: sklearn |
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tags: |
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- text-classification |
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- sklearn |
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- phishing |
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- url |
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- onnx |
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model_format: pickle |
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model_file: model.pkl |
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inference: false |
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pipeline_tag: text-classification |
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datasets: |
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- pirocheto/phishing-url |
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--- |
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# Model Description |
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The model predicts the probability that a URL is a phishing site. |
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To understand what phishing is, refer to the Wikipedia page: |
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[https://en.wikipedia.org/wiki/Phishing](https://en.wikipedia.org/wiki/Phishing) |
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-- this is not a phishing link 😜 |
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- **Model type:** LinearSVM |
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- **Task:** Binary classification |
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- **License:** MIT |
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- **Repository:** https://github.com/pirocheto/phishing-url-detection |
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## Evaluation |
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| Metric | Value | |
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|-----------|----------| |
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| roc_auc | 0.986844 | |
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| accuracy | 0.948568 | |
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| f1 | 0.948623 | |
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| precision | 0.947619 | |
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| recall | 0.949629 | |
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# How to Get Started with the Model |
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Using pickle in Python is discouraged due to security risks during data deserialization, potentially allowing code injection. |
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It lacks portability across Python versions and interoperability with other languages. |
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Read more about this subject in the [Hugging Face Documentation](https://huggingface.co/docs/hub/security-pickle). |
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Instead, we recommend using the ONNX model, which is more secure. |
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In addition to being lighter and faster, it can be utilized by languages supported by the [ONNX runtime](https://onnxruntime.ai/docs/get-started/). |
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Below are some examples to get you start. For others languages please refer to the ONNX documentation |
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<details> |
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<summary><b>Python</b> - ONNX - [recommended 👍]</summary> |
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```python |
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import numpy as np |
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import onnxruntime |
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from huggingface_hub import hf_hub_download |
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REPO_ID = "pirocheto/phishing-url-detection" |
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FILENAME = "model.onnx" |
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model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) |
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# Initializing the ONNX Runtime session with the pre-trained model |
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sess = onnxruntime.InferenceSession( |
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model_path, |
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providers=["CPUExecutionProvider"], |
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) |
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urls = [ |
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"https://clubedemilhagem.com/home.php", |
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"http://www.medicalnewstoday.com/articles/188939.php", |
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] |
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inputs = np.array(urls, dtype="str") |
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# Using the ONNX model to make predictions on the input data |
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results = sess.run(None, {"inputs": inputs})[1] |
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for url, proba in zip(urls, results): |
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print(f"URL: {url}") |
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print(f"Likelihood of being a phishing site: {proba[1] * 100:.2f} %") |
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print("----") |
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``` |
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</details> |
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<details> |
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<summary><b>NodeJS</b>- ONNX - [recommended 👍]</summary> |
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```javascript |
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const ort = require('onnxruntime-node'); |
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async function main() { |
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try { |
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// Make sure you have downloaded the model.onnx |
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// Creating an ONNX inference session with the specified model |
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const model_path = "./model.onnx"; |
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const session = await ort.InferenceSession.create(model_path); |
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const urls = [ |
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"https://clubedemilhagem.com/home.php", |
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"http://www.medicalnewstoday.com/articles/188939.php", |
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] |
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// Creating an ONNX tensor from the input data |
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const tensor = new ort.Tensor('string', urls, [urls.length,]); |
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// Executing the inference session with the input tensor |
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const results = await session.run({"inputs": tensor}); |
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const probas = results['probabilities'].data; |
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// Displaying results for each URL |
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urls.forEach((url, index) => { |
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const proba = probas[index * 2 + 1]; |
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const percent = (proba * 100).toFixed(2); |
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console.log(`URL: ${url}`); |
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console.log(`Likelihood of being a phishing site: ${percent}%`); |
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console.log("----"); |
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}); |
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} catch (e) { |
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console.log(`failed to inference ONNX model: ${e}.`); |
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} |
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}; |
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main(); |
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``` |
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</details> |
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<details> |
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<summary><b>JavaScript</b> - ONNX - [recommended 👍]</summary> |
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```html |
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<!DOCTYPE html> |
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<html> |
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<header> |
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<title>Get Started with JavaScript</title> |
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</header> |
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<body> |
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<!-- import ONNXRuntime Web from CDN --> |
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<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/ort.min.js"></script> |
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<script> |
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// use an async context to call onnxruntime functions. |
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async function main() { |
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try { |
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const model_path = "./model.onnx"; |
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const session = await ort.InferenceSession.create(model_path); |
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const urls = [ |
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"https://clubedemilhagem.com/home.php", |
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"http://www.medicalnewstoday.com/articles/188939.php", |
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]; |
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// Creating an ONNX tensor from the input data |
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const tensor = new ort.Tensor("string", urls, [urls.length]); |
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// Executing the inference session with the input tensor |
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const results = await session.run({ inputs: tensor }); |
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const probas = results["probabilities"].data; |
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// Displaying results for each URL |
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urls.forEach((url, index) => { |
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const proba = probas[index * 2 + 1]; |
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const percent = (proba * 100).toFixed(2); |
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document.write(`URL: ${url} <br>`); |
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document.write( |
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`Likelihood of being a phishing site: ${percent} % <br>` |
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); |
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document.write("---- <br>"); |
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}); |
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} catch (e) { |
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document.write(`failed to inference ONNX model: ${e}.`); |
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} |
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} |
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main(); |
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</script> |
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</body> |
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</html> |
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``` |
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</details> |
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<details> |
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<summary><b>Python</b> - Pickle - [not recommended ⚠️]</summary> |
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```python |
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import joblib |
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from huggingface_hub import hf_hub_download |
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REPO_ID = "pirocheto/phishing-url-detection" |
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FILENAME = "model.pkl" |
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# Download the model from the Hugging Face Model Hub |
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model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) |
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urls = [ |
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"https://clubedemilhagem.com/home.php", |
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"http://www.medicalnewstoday.com/articles/188939.php", |
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] |
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# Load the downloaded model using joblib |
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model = joblib.load(model_path) |
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# Predict probabilities for each URL |
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probas = model.predict_proba(urls) |
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for url, proba in zip(urls, probas): |
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print(f"URL: {url}") |
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print(f"Likelihood of being a phishing site: {proba[1] * 100:.2f} %") |
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print("----") |
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``` |
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</details> |
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