
import pandas as pd
import numpy as np
from pandas import Series,DataFrame


df_baseline_1 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042219_1000.pcap_Flow.csv", header=0)


df_baseline_2 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042319_1000_00000_20190423090037.pcap_Flow.csv", header=0)


df_baseline_3 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042319_1000_00001_20190423112114.pcap_Flow.csv", header=0)


df_baseline_4 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042319_1000_00002_20190423142733.pcap_Flow.csv", header=0)


df_baseline_5 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042319_1000_00003_20190423172324.pcap_Flow.csv", header=0)


df_baseline_6 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042319_1000_00004_20190423204716.pcap_Flow.csv", header=0)


df_baseline_7 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042319_1000_00005_20190423234420.pcap_Flow.csv", header=0)


df_baseline_8 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042319_1000_00006_20190424024732.pcap_Flow.csv", header=0)


df_baseline_9 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042319_1000_00007_20190424060257.pcap_Flow.csv", header=0)


df_baseline_10 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042419_1000_00000_20190424090033.pcap_Flow.csv", header=0)


df_baseline_11 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042419_1000_00001_20190424120953.pcap_Flow.csv", header=0)


df_baseline_12 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042419_1000_00002_20190424152735.pcap_Flow.csv", header=0)


df_baseline_13 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042419_1000_00003_20190424184622.pcap_Flow.csv", header=0)


df_baseline_14 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042419_1000_00004_20190424212615.pcap_Flow.csv", header=0)


df_baseline_15 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042419_1000_00005_20190425004512.pcap_Flow.csv", header=0)


df_baseline_16 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042419_1000_00006_20190425033458.pcap_Flow.csv", header=0)


df_baseline_17 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/042419_1000_00007_20190425062020.pcap_Flow.csv", header=0)


df_baseline_18 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/102519_00000_20191024083134.pcap_Flow.csv", header=0)


df_baseline_19 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/102519_00001_20191024111356.pcap_Flow.csv", header=0)


df_baseline_20 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/102519_00002_20191024134814.pcap_Flow.csv", header=0)


df_baseline_21 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/102519_00003_20191024170045.pcap_Flow.csv", header=0)


df_baseline_22 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/102519_00004_20191024195649.pcap_Flow.csv", header=0)


df_baseline_23 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/102519_00005_20191024224425.pcap_Flow.csv", header=0)


df_baseline_24 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/102519_00006_20191025013841.pcap_Flow.csv", header=0)


df_baseline_25 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/102519_00007_20191025044455.pcap_Flow.csv", header=0)


df_baseline_26 = pd.read_csv("/home/z/Downloads/CUPID-Baselines-CICFlowMeter/102519_00008_20191025075121.pcap_Flow.csv", header=0)


df_baselines = pd.concat([df_baseline_1, df_baseline_2, df_baseline_3, df_baseline_4, df_baseline_5, df_baseline_6, df_baseline_7, df_baseline_8, df_baseline_9, df_baseline_10, df_baseline_11, df_baseline_12, df_baseline_13, df_baseline_14, df_baseline_15, df_baseline_16, df_baseline_17, df_baseline_18, df_baseline_19, df_baseline_20, df_baseline_21, df_baseline_22, df_baseline_23, df_baseline_24, df_baseline_25, df_baseline_26], axis=0)


df_baselines['Label']=0


df_baselines.to_csv(r'/home/z/Python/jupyter-notebooks/CUPID-Baselines-Labeled.csv')


df_human = pd.read_csv("/home/z/Python/jupyter-notebooks/CUPID-Human-Surfing-Queen.csv", header=0)


df_human['Label']=0


df_dvwa = pd.read_csv("/home/z/Python/jupyter-notebooks/CUPID-Human-DVWA-Queen.csv", header=0)


# Label based on the IP addresses. P2_DVWA and P10_DVWA have ip.src==10.10.10.2
def bad(x):
    if x == '10.10.10.1':
        return 1
    if x == '10.10.10.2':
        return 1
    else:
        return 0


# Apply the criteria to a new column called 'label'
df_dvwa['Label'] = df_dvwa['Src IP'].apply(bad)


df_human = pd.concat([df_human, df_dvwa])


df_human.to_csv(r'/home/z/Python/jupyter-notebooks/CUPID-Human-Labeled.csv')


df_auto = pd.read_csv("/home/z/Python/jupyter-notebooks/CUPID-Auto-Queen.csv", header=0)


# Label based on the IP addresses.
def bad(x):
    if x == '10.10.10.13':
        return 1
    if x == '10.10.10.18':
        return 1
    if x == '10.10.10.19':
        return 1
    if x == '0.0.0.0':
        return 1
    else:
        return 0


df_auto['Label'] = df_auto['Src IP'].apply(bad)


df_auto.to_csv(r'/home/z/Python/jupyter-notebooks/CUPID-Auto-Labeled.csv')


# Count the instances of malicious traffic to benign traffic
df_auto.groupby('Label').count()

	Flow ID	Src IP	Src Port	Dst IP	Dst Port	Protocol	Timestamp	Flow Duration	Total Fwd Packet	Total Bwd packets	...	Fwd Act Data Pkts	Fwd Seg Size Min	Active Mean	Active Std	Active Max	Active Min	Idle Mean	Idle Std	Idle Max	Idle Min
Label
0	74695	74695	74695	74695	74695	74695	74695	74695	74695	74695	...	74695	74695	74695	74695	74695	74695	74695	74695	74695	74695
1	114873	114873	114873	114873	114873	114873	114873	114873	114873	114873	...	114873	114873	114873	114873	114873	114873	114873	114873	114873	114873

Import and Label Baselines¶

Import and Label Human Surfing¶

Import and Label Auto¶