增加中期实验数据,代码,ppt
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崔一鸣
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Experiment/statFeature/StatFeature.ipynb
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615
Experiment/statFeature/StatFeature.ipynb
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Experiment/statFeature/csvFile/2019-12-20_21/examples.csv
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Experiment/statFeature/csvFile/2019-12-20_21/examples.csv
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from sklearn.linear_model import LogisticRegression
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from sklearn.svm import SVC
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from sklearn.naive_bayes import GaussianNB
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from sklearn import tree
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import f1_score,recall_score,precision_score
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import random
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import matplotlib.pyplot as plt
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%matplotlib inline
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PREFIX_DIR = "/Users/Leo/Documents/github/GradProj/"
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def RF():
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classifer = RandomForestClassifier()
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classifer.fit(x_train, y_train)
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y_pred = classifer.predict(x_test)
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f1_score_list.append(f1_score(y_test, y_pred, average='micro'))
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recall_score_list.append(recall_score(y_test, y_pred, average='micro'))
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precision_score_list.append(precision_score(y_test, y_pred, average='micro'))
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scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]
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score_df.loc['RandomForest'] = scores
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score_df.plot.bar()
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print(scores)
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def main():
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date = sys.argv[1]
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example_csv_file = PREFIX_DIR + 'Experiment/statFeature/csvFile/' + date + '/examples.csv'
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examples_df = pd.read_csv(example_csv_file)
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class_counts = examples_df['label'].value_counts().plot.bar()
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examples = examples_df.values.copy()
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score_df = pd.DataFrame(np.zeros((5,3)),index = ['LogisticRegression', 'SVM', 'GaussianNB', 'tree', 'RandomForest'], \
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columns = ['precision', 'recall', 'f1'])
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f1_score_list = list()
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recall_score_list = list()
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precision_score_list = list()
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for i in range(50):
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np.random.shuffle(examples)
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examples_train = examples[:int(len(examples)*0.75)]
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examples_test = examples[int(len(examples)*0.75):]
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x_train = examples_train[:,0:-1]
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y_train = examples_train[:,-1]
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x_test = examples_test[:,0:-1]
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y_test = examples_test[:,-1]
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RF(score_df, f1_score_list, recall_score_list, precision_score_list, \
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x_train, y_train, x_test, y_test)
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if __name__ == '__main__':
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main()
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@@ -1,124 +0,0 @@
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import sys
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import os
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import json
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import pandas as pd
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import numpy as np
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PREFIX_DIR = "/Users/Leo/Documents/github/GradProj/"
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def label_dict_build(date):
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example_label_file = PREFIX_DIR + 'DataSet/result/' + date + '/stream_tag.txt'
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example_label_df = pd.read_table(example_label_file, sep='\s+', header=None)
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example_label_df[3] = 443
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example_label = {tuple(example_label_df.iloc[i,0:4].values):example_label_df.iloc[i,4] for i in example_label_df.index}
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return example_label
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def main():
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date = sys.argv[1]
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example_label = label_dict_build(date)
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row_count = 1771
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cloumn_count = 25
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example_json_file = PREFIX_DIR + 'DataSet/result/' + date + '/stream_feature.txt'
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example_json_f = open(example_json_file, 'r')
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array_shape = (row_count, cloumn_count)
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result_data = np.zeros(array_shape)
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result_label = list()
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i = 0
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for line in example_json_f.readlines():
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example_json = json.loads(line)
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#标签
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try:
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flow_key = (example_json['sip'], example_json['sport'], example_json['dip'], example_json['dport'])
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result_label.append(example_label[flow_key])
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except Exception:
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continue
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#统计特征
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packets = example_json['packets']
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c2s_packets_bytes = list()
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s2c_packets_bytes = list()
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c2s_packets_intervals = list()
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s2c_packets_intervals = list()
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for packet in packets:
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if packet['dir'] == 1:
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c2s_packets_bytes.append(packet['bytes'])
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c2s_packets_intervals.append(packet['interval'])
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elif packet['dir'] == 2:
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s2c_packets_bytes.append(packet['bytes'])
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s2c_packets_intervals.append(packet['interval'])
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c2s_bytes = example_json['c2s_bytes']
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s2c_bytes = example_json['s2c_bytes']
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c2s_pkts = example_json['c2s_pkts']
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s2c_pkts = example_json['s2c_pkts']
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duration = example_json['duration']
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c2s_packets_bytes_mean = 0
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c2s_packets_bytes_median = 0
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c2s_packets_bytes_std = 0
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c2s_packets_bytes_max = 0
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c2s_packets_bytes_min = 0
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c2s_packets_intervals_mean = 0
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c2s_packets_intervals_median = 0
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c2s_packets_intervals_std = 0
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c2s_packets_intervals_max = 0
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c2s_packets_intervals_min = 0
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s2c_packets_bytes_mean = 0
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s2c_packets_bytes_median = 0
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s2c_packets_bytes_std = 0
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s2c_packets_bytes_max = 0
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s2c_packets_bytes_min = 0
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s2c_packets_intervals_mean = 0
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s2c_packets_intervals_median = 0
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s2c_packets_intervals_std = 0
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s2c_packets_intervals_max = 0
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s2c_packets_intervals_min = 0
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if c2s_bytes > 0:
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c2s_packets_bytes_mean = np.mean(c2s_packets_bytes)
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c2s_packets_bytes_median = np.median(c2s_packets_bytes)
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c2s_packets_bytes_std = np.std(c2s_packets_bytes)
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c2s_packets_bytes_max = np.max(c2s_packets_bytes)
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c2s_packets_bytes_min = np.min(c2s_packets_bytes)
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c2s_packets_intervals_mean = np.mean(c2s_packets_intervals)
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c2s_packets_intervals_median = np.median(c2s_packets_intervals)
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c2s_packets_intervals_std = np.std(c2s_packets_intervals)
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c2s_packets_intervals_max = np.max(c2s_packets_intervals)
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c2s_packets_intervals_min = np.min(c2s_packets_intervals)
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if s2c_bytes > 0:
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s2c_packets_bytes_mean = np.mean(s2c_packets_bytes)
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s2c_packets_bytes_median = np.median(s2c_packets_bytes)
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s2c_packets_bytes_std = np.std(s2c_packets_bytes)
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s2c_packets_bytes_max = np.max(s2c_packets_bytes)
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s2c_packets_bytes_min = np.min(s2c_packets_bytes)
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s2c_packets_intervals_mean = np.mean(s2c_packets_intervals)
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s2c_packets_intervals_median = np.median(s2c_packets_intervals)
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s2c_packets_intervals_std = np.std(s2c_packets_intervals)
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s2c_packets_intervals_max = np.max(s2c_packets_intervals)
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s2c_packets_intervals_min = np.min(s2c_packets_intervals)
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result = [c2s_bytes, c2s_pkts, s2c_bytes, s2c_pkts, duration, c2s_packets_bytes_mean, c2s_packets_bytes_median, c2s_packets_bytes_std,\
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c2s_packets_bytes_max, c2s_packets_bytes_min, c2s_packets_intervals_mean, c2s_packets_intervals_median, c2s_packets_intervals_std,\
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c2s_packets_intervals_max, c2s_packets_intervals_min, s2c_packets_bytes_mean, s2c_packets_bytes_median, s2c_packets_bytes_std,\
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s2c_packets_bytes_max, s2c_packets_bytes_min, s2c_packets_intervals_mean, s2c_packets_intervals_median, s2c_packets_intervals_std,\
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s2c_packets_intervals_max, s2c_packets_intervals_min]
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result_data[i,:] = result
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i += 1
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print('row = ' + str(row_count))
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print("result_label = " + str(len(result_label)))
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base_head = ['c2s_bytes', 'c2s_pkts', 's2c_bytes', 's2c_pkts', 'duration', 'c2s_packets_bytes_mean', 'c2s_packets_bytes_median', 'c2s_packets_bytes_std',\
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'c2s_packets_bytes_max', 'c2s_packets_bytes_min', 'c2s_packets_intervals_mean', 'c2s_packets_intervals_median', 'c2s_packets_intervals_std',\
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'c2s_packets_intervals_max', 'c2s_packets_intervals_min', 's2c_packets_bytes_mean', 's2c_packets_bytes_median', 's2c_packets_bytes_std',\
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's2c_packets_bytes_max', 's2c_packets_bytes_min', 's2c_packets_intervals_mean', 's2c_packets_intervals_median', 's2c_packets_intervals_std',\
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's2c_packets_intervals_max', 's2c_packets_intervals_min']
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header = base_head
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result_df = pd.DataFrame(result_data, columns=header)
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result_df['label'] = np.array(result_label)
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example_csv_file = PREFIX_DIR + 'Experiment/statFeature/csvFile/' + date + '/examples.csv'
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result_df.to_csv(example_csv_file, index=False)
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if __name__ == '__main__':
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main()
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