mirror of
https://github.com/vel21ripn/nDPI.git
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65 lines
2.3 KiB
Bash
Executable file
65 lines
2.3 KiB
Bash
Executable file
#!/bin/sh
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cd "$(dirname "${0}")"
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# Baseline performances ------------------------------------------------------------------------------------------------
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# Important notes: BASE values must be integers examples and represents percentage (e.g. 79%, 98%).
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BASE_ACCURACY=69
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BASE_PRECISION=89
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BASE_RECALL=41
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# ----------------------------------------------------------------------------------------------------------------------
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DGA_EVALUATE="./dga/dga_evaluate"
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DGA_DATA="dga/test_dga.csv"
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NON_DGA_DATA="dga/test_non_dga.csv"
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DGA_DATA_SIZE=0
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NON_DGA_DATA_SIZE=0
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DATA_SIZE=0
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RC=0
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get_evaluation_data_size() {
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DGA_DATA_SIZE=`wc -l dga/test_dga.csv | awk '{split($0,a," "); print a[1]}'`
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NON_DGA_DATA_SIZE=`wc -l dga/test_non_dga.csv | awk '{split($0,a," "); print a[1]}'`
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DATA_SIZE=$(( $NON_DGA_DATA_SIZE + $DGA_DATA_SIZE ))
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}
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evaluate_ndpi_dga_detection() {
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# DGA detection is a binary classification problem, We evaluate the following metrics:
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# Accuracy: (TP + TN) / (TP + TN + FN + FP)
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# Precision: TP / (TP + FP)
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# Recall: TP / (TP + FN)
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TP=`$DGA_EVALUATE dga/test_dga.csv`
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FN=$(( $DGA_DATA_SIZE - $TP ))
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FP=`$DGA_EVALUATE dga/test_non_dga.csv`
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TN=$(( $NON_DGA_DATA_SIZE - $FP ))
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ACCURACY=`echo "print(int(((${TP} + ${TN})/(${TP} + ${TN} + ${FP} + ${FN}))*100))" | python3`
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PRECISION=`echo "print(int(((${TP})/(${TP} + ${FP}))*100))" | python3`
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RECALL=`echo "print(int(((${TP})/(${TP} + ${FN}))*100))" | python3`
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# In case modified version of classification algorithm decreases performances, test do not pass.
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if [ $ACCURACY -lt $BASE_ACCURACY ]; then
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printf "ERROR: Your modifications decreased DGA classifier accuracy: 0.${BASE_ACCURACY} decreased to 0.${ACCURACY}!\n"
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RC=1
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fi
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if [ $PRECISION -lt $BASE_PRECISION ]; then
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printf "ERROR: Your modifications decreased DGA classifier precision: 0.${BASE_PRECISION} decreased to 0.${PRECISION}!\n"
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RC=1
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fi
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if [ $RECALL -lt $BASE_RECALL ]; then
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printf "ERROR: Your modifications decreased DGA classifier recall: 0.${BASE_RECALL} decreased to 0.${RECALL}!\n"
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RC=1
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fi
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# Finally we print the current performances, upgrade BASE_ metrics in case you improved it.
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echo "DGA detection performances report:"
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echo "Accuracy=0.$ACCURACY"
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echo "Precision=0.$PRECISION"
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echo "Recall=0.$RECALL"
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}
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get_evaluation_data_size
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evaluate_ndpi_dga_detection
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exit $RC
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