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m-tfidf-np.py
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m-tfidf-np.py
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from numpy.ma import count
import pandas as pd
from pandas import DataFrame
import gensim
from gensim.parsing.preprocessing import preprocess_documents
import utils
from gensim.models.coherencemodel import CoherenceModel
import csv
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
import numpy as np, random
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.metrics import classification_report
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from imblearn.over_sampling import SMOTE
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import roc_auc_score
from sklearn.naive_bayes import GaussianNB
from sklearn import svm
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import roc_auc_score
import warnings
warnings.filterwarnings("ignore")
# implements TF-IDF
def main():
pd.set_option('display.max_columns', None)
np.random.RandomState(0)
dfTactics = pd.read_excel('attack-data.xlsx', sheet_name=0)
dfTechniques = pd.read_excel('attack-data.xlsx', sheet_name=2)
dfProcedures = pd.read_excel('attack-data.xlsx', sheet_name=3)
dfTacticsCut = dfTactics.loc[:, ['ID', 'name', 'description']]
dfTacticsCut['type'] = 'tactics'
dfTechniquesCut = dfTechniques.loc[:, ['ID', 'name', 'description']]
dfTechniquesCut['type'] = 'techniques'
dfTechniqueProcedureMerged = pd.merge(dfTechniques, dfProcedures, left_on='ID', right_on='target ID')
dfProceduresCut = dfTechniqueProcedureMerged.loc[:, ['source ID', 'name', 'mapping description']]
dfProceduresCut['ID'] = dfProceduresCut['source ID']
dfProceduresCut['description'] = dfProceduresCut['mapping description']
dfProceduresCut['type'] = 'example'
dfProceduresCut = dfProceduresCut.loc[:, ['ID', 'name', 'description', 'type']]
dataframe = pd.concat([dfTacticsCut, dfTechniquesCut, dfProceduresCut], ignore_index=True)
trainAndTestSet = dataframe.loc[dataframe['type'] == 'example']
trainAndTestSet['name'] = trainAndTestSet['name'].apply(utils.splitTechniqueName)
techniqueNamesWithLessThanFiveExamples = []
trainAndTestSetGrouped = trainAndTestSet.groupby('name')
classCounts = []
for name,group in trainAndTestSetGrouped:
classCounts.append({ 'name' : f'{name}', 'count' : group.shape[0]})
if group.shape[0] < 30:
techniqueNamesWithLessThanFiveExamples.append(name)
file = open('NPChunks_final_result.txt', 'w')
for top_n_class in [2, 4, 8, 16, 32, 64]:
file.write('\n=================\n')
file.write(f'n = {top_n_class}\n')
print(f'n = {top_n_class}\n')
classCounts_sorted = sorted(classCounts, key = lambda x:x['count'], reverse = True)[0:top_n_class]
classCounts_top_n = [item['name'] for item in classCounts_sorted]
trainAndTestSetFiltered = trainAndTestSet[trainAndTestSet['name'].isin(classCounts_top_n)]
text_corpus = trainAndTestSetFiltered['description'].values
text_corpus = utils.removeURLandCitationBulk(text_corpus)
out = utils.filterNPsFromCorpus(text_corpus)
text_corpus = out[0]
processed_corpus = preprocess_documents(text_corpus)
list = []
for item in processed_corpus:
text = ' '.join(item)
list.append(text.strip())
trainAndTestSetFiltered['description'] = list
vectorizer = TfidfVectorizer(use_idf=True)
vectors = vectorizer.fit_transform(trainAndTestSetFiltered['description'])
feature_names = vectorizer.get_feature_names()
feature_names = ['feature-' + feature_name for feature_name in feature_names]
dense = vectors.todense()
denselist = dense.tolist()
df = pd.DataFrame(denselist, columns=feature_names)
trainAndTestSetFiltered = pd.concat([trainAndTestSetFiltered.reset_index(drop=True), df.reset_index(drop=True)], axis=1)
skf = StratifiedKFold(n_splits=5)
target = trainAndTestSetFiltered.loc[:,'name']
train = []
test = []
for train_index, test_index in skf.split(trainAndTestSetFiltered, target):
train.append(trainAndTestSetFiltered.iloc[train_index])
test.append(trainAndTestSetFiltered.iloc[test_index])
for item in ['knn', 'nb', 'svm', 'rf', 'dt', 'nn']:
file.write('\n###################\n')
file.write(f'classifier: {item}')
print(f'classifier: {item}')
accuracy = []
precision_m = []
precision_w = []
recall_m = []
recall_w = []
f1_m = []
f1_w = []
auc = []
for index in range(0, 5):
numOfColumns = len(train[index].columns)
clf = None
if item == 'knn': clf = KNeighborsClassifier().fit(train[index].iloc[:, 4:(numOfColumns)], train[index]['name'])
if item == 'nn': clf = MLPClassifier().fit(train[index].iloc[:, 4:(numOfColumns)], train[index]['name'])
if item == 'nb': clf = GaussianNB().fit(train[index].iloc[:, 4:(numOfColumns)], train[index]['name'])
if item == 'svm': clf = svm.SVC(probability=True).fit(train[index].iloc[:, 4:(numOfColumns)], train[index]['name'])
if item == 'dt': clf = DecisionTreeClassifier().fit(train[index].iloc[:, 4:(numOfColumns)], train[index]['name'])
if item == 'rf': clf = RandomForestClassifier().fit(train[index].iloc[:, 4:(numOfColumns)], train[index]['name'])
predicted = clf.predict(test[index].iloc[:, 4:(numOfColumns)])
output = classification_report(test[index]['name'], predicted, output_dict = True)
probs = clf.predict_proba(test[index].iloc[:, 4:(numOfColumns)])
if top_n_class == 2:
auc.append(roc_auc_score( test[index]['name'] , probs[:,1]))
else:
auc.append(roc_auc_score( test[index]['name'] , probs , multi_class='ovr', average='weighted'))
accuracy.append(output['accuracy'])
precision_m.append(output['macro avg']['precision'])
precision_w.append(output['weighted avg']['precision'])
recall_m.append(output['macro avg']['recall'])
recall_w.append(output['weighted avg']['recall'])
f1_m.append(output['macro avg']['f1-score'])
f1_w.append(output['weighted avg']['f1-score'])
file.write(f'accuracy: {sum(accuracy)/5}\n')
file.write(f'precision macro: {sum(precision_m)/5}\n')
file.write(f'precision weighted: {sum(precision_w)/5}\n')
file.write(f'recall macro: {sum(recall_m)/5}\n')
file.write(f'recall weighted: {sum(recall_w)/5}\n')
file.write(f'f1 macro: {sum(f1_m)/5}\n')
file.write(f'f1 weighted: {sum(f1_w)/5}\n')
file.write(f'auc: {sum(auc)/5}\n')
file.write('###################\n')
file.close()
if __name__ == "__main__":
main()