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A Machine Learning Web-based Attack Classifier to detect and identify LFI, RFI, SQLI, and XSS attacks using the request paths
Readme
A Machine Learning Web-based Attack Classifier for the purpose of detecting and identifying LFI, RFI, SQLI, and XSS attacks based on request paths. This initiative is part of a research project at the University of Amsterdam conducted by Jord and Isaac under the supervision of Evgeniia.
pip install ml-wac
Performance evaluations have been conducted on all models to determine their accuracy and inference time on the 3579-item test set.
Model Name | Accuracy | Inference Time (ms) |
---|---|---|
XGBoost | 98.80% | 82.71 |
Decision Tree | 98.21% | 1.95 |
Logistic Regression | 98.30% | 5.58 |
Support Vector Machine | 99.25% | 19225.70 |
from ml_wac.wac import WebAttackClassifier
# Create new instance
wac = WebAttackClassifier()
# Predict a single path. Optionally, a certainty threshold can be provided
prediction = wac.predict_single("/test?id=<script>alert(1)</script>", threshold=0.7)
print(prediction)
from ml_wac.wac import WebAttackClassifier
# Create new instance
wac = WebAttackClassifier()
# Predict a list of paths, returns a list of predicted attack types
predictions = wac.predict([
"/status?message=<script>/*+Bad+stuff+here+*/</script>",
"/?download=../include/connection.php",
"/?file=../../uploads/evil.php",
"/products?category=Gifts'+OR+1=1--"
])
print(predictions)
Use one of the other pre-trained models for inference. By default the logistic regression model is used.
from ml_wac.types.model_type import ModelType
from ml_wac.wac import WebAttackClassifier
# Load the XG_BOOST model
wac = WebAttackClassifier(model_type=ModelType.XG_BOOST)
# Predict a single path. Optionally, a certainty threshold can be provided
prediction = wac.predict_single("/test?id=<script>alert(1)</script>", threshold=0.7)
print(prediction)
FAQs
A Machine Learning Web-based Attack Classifier to detect and identify LFI, RFI, SQLI, and XSS attacks using the request paths
We found that ml-wac demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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