ml-wac 1.1

A Machine Learning Web-based Attack Classifier to detect and identify LFI, RFI, SQLI, and XSS attacks using the request paths

WAC: ML Web-based Attack Classifier

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.

Getting started

Installation using PyPI

pip install ml-wac

Performance

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

Examples

Predict a single path

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)

Predict multiple paths

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 other trained models

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)