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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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- classification |
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- phishing |
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--- |
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# Model description |
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## Training Procedure |
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### Hyperparameters |
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<details> |
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<summary> Click to expand </summary> |
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| Hyperparameter | Value | |
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|-------------------------------------------------------------|--------------------------| |
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| memory | | |
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| steps | [('standardscaler', StandardScaler()), ('calibratedclassifiercv', CalibratedClassifierCV(cv=5, estimator=RandomForestClassifier(),<br /> method='isotonic'))] | |
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| verbose | False | |
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| standardscaler | StandardScaler() | |
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| calibratedclassifiercv | CalibratedClassifierCV(cv=5, estimator=RandomForestClassifier(),<br /> method='isotonic') | |
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| standardscaler__copy | True | |
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| standardscaler__with_mean | True | |
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| standardscaler__with_std | True | |
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| calibratedclassifiercv__base_estimator | deprecated | |
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| calibratedclassifiercv__cv | 5 | |
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| calibratedclassifiercv__ensemble | True | |
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| calibratedclassifiercv__estimator__bootstrap | True | |
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| calibratedclassifiercv__estimator__ccp_alpha | 0.0 | |
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| calibratedclassifiercv__estimator__class_weight | | |
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| calibratedclassifiercv__estimator__criterion | gini | |
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| calibratedclassifiercv__estimator__max_depth | | |
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| calibratedclassifiercv__estimator__max_features | sqrt | |
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| calibratedclassifiercv__estimator__max_leaf_nodes | | |
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| calibratedclassifiercv__estimator__max_samples | | |
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| calibratedclassifiercv__estimator__min_impurity_decrease | 0.0 | |
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| calibratedclassifiercv__estimator__min_samples_leaf | 1 | |
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| calibratedclassifiercv__estimator__min_samples_split | 2 | |
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| calibratedclassifiercv__estimator__min_weight_fraction_leaf | 0.0 | |
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| calibratedclassifiercv__estimator__n_estimators | 100 | |
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| calibratedclassifiercv__estimator__n_jobs | | |
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| calibratedclassifiercv__estimator__oob_score | False | |
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| calibratedclassifiercv__estimator__random_state | | |
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| calibratedclassifiercv__estimator__verbose | 0 | |
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| calibratedclassifiercv__estimator__warm_start | False | |
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| calibratedclassifiercv__estimator | RandomForestClassifier() | |
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| calibratedclassifiercv__method | isotonic | |
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| calibratedclassifiercv__n_jobs | | |
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</details> |
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### Model Plot |
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This is the architecture of the model loaded by joblib. |
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<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('standardscaler', StandardScaler()),('calibratedclassifiercv',CalibratedClassifierCV(cv=5,estimator=RandomForestClassifier(),method='isotonic'))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('standardscaler', StandardScaler()),('calibratedclassifiercv',CalibratedClassifierCV(cv=5,estimator=RandomForestClassifier(),method='isotonic'))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">calibratedclassifiercv: CalibratedClassifierCV</label><div class="sk-toggleable__content"><pre>CalibratedClassifierCV(cv=5, estimator=RandomForestClassifier(),method='isotonic')</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-9" type="checkbox" ><label for="sk-estimator-id-9" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: RandomForestClassifier</label><div class="sk-toggleable__content"><pre>RandomForestClassifier()</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-10" type="checkbox" ><label for="sk-estimator-id-10" class="sk-toggleable__label sk-toggleable__label-arrow">RandomForestClassifier</label><div class="sk-toggleable__content"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div> |
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## Evaluation Results |
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| Metric | Value | |
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|-----------|----------| |
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| accuracy | 0.945652 | |
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| f1-score | 0.945114 | |
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| precision | 0.951996 | |
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| recall | 0.938331 | |
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## Test Report |
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## Model Interpretation |
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### Feature Importances |
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# How to Get Started with the Model |
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Below are some code snippets to load the model. |
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## With joblib (not recommended) |
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```python |
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import joblib |
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import pandas as pd |
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urls = [ |
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{ |
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"url": "https://www.rga.com/about/workplace", |
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"nb_hyperlinks": 97.0, |
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"ratio_intHyperlinks": 0.969072165, |
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"ratio_extHyperlinks": 0.030927835, |
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"ratio_extRedirection": 0.0, |
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"safe_anchor": 25.0, |
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"domain_registration_length": 3571.0, |
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"domain_age": 11039, |
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"web_traffic": 178542.0, |
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"google_index": 0.0, |
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"page_rank": 5, |
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}, |
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] |
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model = joblib.load("models/model.pkl") |
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df = pd.DataFrame(urls) |
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df = df.set_index("url") |
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probas = model.predict_proba(df.values) |
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for url, proba in zip(urls, probas): |
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print(f"URL: {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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# output: |
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# URL: https://www.rga.com/about/workplace |
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# Likelihood of being a phishing site: 0.89% |
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# ---- |
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``` |
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## With ONNX (recommended) |
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### Python |
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```python |
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import numpy as np |
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import onnxruntime |
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import pandas as pd |
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# Defining a list of URLs with characteristics |
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urls = [ |
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{ |
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"url": "https://www.rga.com/about/workplace", |
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"nb_hyperlinks": 97.0, |
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"ratio_intHyperlinks": 0.969072165, |
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"ratio_extHyperlinks": 0.030927835, |
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"ratio_extRedirection": 0.0, |
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"safe_anchor": 25.0, |
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"domain_registration_length": 3571.0, |
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"domain_age": 11039, |
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"web_traffic": 178542.0, |
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"google_index": 0.0, |
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"page_rank": 5, |
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}, |
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] |
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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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"models/model.onnx", |
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providers=["CPUExecutionProvider"], |
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) |
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# Creating a DataFrame from the list of URLs |
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df = pd.DataFrame(urls) |
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df = df.set_index("url") |
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# Converting DataFrame data to a float32 NumPy array |
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inputs = df.astype(np.float32).to_numpy() |
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# Using the ONNX model to make predictions on the input data |
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probas = sess.run(None, {"X": inputs})[1] |
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# Displaying the results |
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for url, proba in zip(urls, probas): |
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print(proba) |
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print(f"URL: {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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# output: |
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# URL: https://www.rga.com/about/workplace |
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# Likelihood of being a phishing site: 0.89% |
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# ---- |
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``` |
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### JavaScript |
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```javascript |
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const ort = require('onnxruntime-node'); |
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const urls = [ |
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{ |
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"url": "http://rapidpaws.com/wp-content/we_transfer/index2.php?email=/", |
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"nb_hyperlinks": 1, |
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"ratio_intHyperlinks": 1, |
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"ratio_extHyperlinks": 0, |
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"ratio_extRedirection": 0, |
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"safe_anchor": 0, |
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"domain_registration_length": 338, |
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"domain_age": 0, |
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"web_traffic":1853, |
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"google_index": 1, |
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"page_rank": 2, |
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}, |
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]; |
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async function main() { |
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try { |
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// Creating an ONNX inference session with the specified model |
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const model_path = "./models/model.onnx"; |
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const session = await ort.InferenceSession.create(model_path); |
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// Get values from data and remove url links |
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const inputs = urls.map(url => Object.values(url).slice(1)); |
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// Flattening the 2D array to get a 1D array |
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const flattenInputs = inputs.flat(); |
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// Creating an ONNX tensor from the input array |
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const tensor = new ort.Tensor('float32', flattenInputs, [inputs.length, 10]); |
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// Executing the inference session with the input tensor |
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const results = await session.run({"X": tensor}); |
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// Retrieving probability data from the results |
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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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// The index * 2 + 1 is used to access the probability associated with the phishing class |
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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.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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// output: |
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// URL: https://www.rga.com/about/workplace |
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// Likelihood of being a phishing site: 0.89% |
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// ---- |
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``` |
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