Files

OriginalData
Christopher Garner - Identification of Malicious URL’s using Data Mining -Final Report.pdf
FN_Alexa_Rank_1.csv
FN_Country_EQ255.csv
FN_alexa_country.csv
FN_amazon_ancestry.csv
FP_Alexa_Rank_0.csv
FP_Country_EQ255.csv
FP_Country_NE255.csv
README.md
ROC_plot.png
ROC_plot_RForest.png
ROC_plot_TREE.png
alexa.db
benign.csv
benign_describe.csv
bestFeatures.csv
classifierErrorsFN.csv
classifierErrorsFP.csv
cleanAndDedupData.py
cleaned_data.csv
cleaned_deduped.csv
combined_url.csv
confusion_matrix_plot.png
confusion_matrix_plot_RForest.png
confusion_matrix_plot_TREE.png
countries.py
dataAnalysis.py
decisionTree.py
dropped.csv
entropy.py
errorAnalysis.py
extract_features.py
featureAnalysis.py
featuresMask.csv
fileHeader.py
findBestK.py
forestPredictor.sav
invalid.csv
kbest_accuracy.txt
loadAlexaDB.py
malicious_describe.csv
merged_malware.csv
naivePredictor.sav
names.py
predictor.py
randomForest.py
requirements.txt
to_drop.csv
top-1m.csv
trainFullBayesModel.py
trainFullBayesModelAllFeatures.py
treePredictor.sav
tree_render
vector_creator.py

FN_Alexa_Rank_1.csv