WAF

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Web Application Firewall using Machine Learning and Features Engineering

Aref Shaheed, Mhd Bassam Kurdy

Syrian Virtual University - Master of Web Technologies - WPR - S17

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Usage: -d: dataset (1 for HTTPParams, 2 for CSIC 3 for both) -a: algorithm (1 for Naive Bayes, 2 for Logistic Regression, 3 for Decision Tree, 4 for SVM) -t: test percentage (value from 0.05 to 0.95 .. Default is 0.2)

example: waf_training.py -d 1 -a 2 -t 0.2

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