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norrm.py
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import pandas as pd
import numpy as np
from rich.theme import Theme
from rich.console import Console
# dict of rich colors
# color used in project
ct = Theme({
'good': "bold green ",
'bad': "red",
'blue': "blue",
'yellow': "yellow",
'purple': "purple",
'magenta': "magenta",
'cyan': "cyan"
})
rc = Console(record=True, theme=ct)
class Preprocessing:
def __init__(self, df):
self.df = df
self.df.replace([np.inf, -np.inf], np.nan, inplace=True)
self.df.dropna(inplace=True)
def rm_col(self):
del self.df['src_port']
del self.df['mean_bpktl']
del self.df['bpsh_cnt']
del self.df['total_bpktl']
del self.df['mean_active_s']
del self.df['max_active_s']
del self.df['downUpRatio']
del self.df['flow']
del self.df['src']
del self.df['dst']
del self.df['protocol']
del self.df['timestamp']
del self.df['std_biat']
del self.df['furg_cnt']
del self.df['burg_cnt']
del self.df['total_bhlen']
del self.df['flow_cwr']
del self.df['flow_ece']
del self.df['std_active_s']
del self.df['min_active_s']
del self.df['fAvgBytesPerBulk']
del self.df['fAvgPacketsPerBulk']
del self.df['bAvgPacketsPerBulk']
del self.df['fAvgBulkRate']
del self.df['bAvgBytesPerBulk']
del self.df['bAvgBulkRate']
del self.df['mean_biat']
del self.df['min_biat']
del self.df['label']
return
def r_csv(self, filename):
df = pd.read_csv(filename, encoding='utf-8')
return df
def col_rename(self):
self.dict = {'min_idle_s': 'Idle Min',
'max_idle_s': 'Idle Max',
'std_idle_s': 'Idle Std',
'mean_idle_s': 'Idle Mean',
'dst_port': 'Destination Port',
'duration': 'Duration',
'total_fpackets': 'Total Fwd Packets',
'total_bpackets': 'Total Backward Packets',
'total_fpktl': 'Total Length of Fwd Packets',
'min_fpktl': 'Fwd Packet Length Min',
'max_fpktl': 'Fwd Packet Length Max',
'mean_fpktl': 'Fwd Packet Length Mean',
'std_fpktl': 'Fwd Packet Length Std',
'min_bpktl': 'Bwd Packet Length Min',
'max_bpktl': 'Bwd Packet Length Max',
'std_bpktl': 'Bwd Packet Length Std',
'mean_bpktl': 'Bwd Packet Length Mean',
'flowBytesPerSecond': 'Flow Bytes/s',
'flowPktsPerSecond': 'Flow Packets/s',
'mean_flowiat': 'Flow IAT Mean',
'std_flowiat': 'Flow IAT Std',
'max_flowiat': 'Flow IAT Max',
'min_flowiat': 'Flow IAT Min',
'total_fiat': 'Fwd IAT Total',
'mean_fiat': 'Fwd IAT Mean',
'std_fiat': 'Fwd IAT Std',
'max_fiat': 'Fwd IAT Max',
'min_fiat': 'Fwd IAT Min',
'total_biat': 'Bwd IAT Total',
'max_biat': 'Bwd IAT Max',
'fpsh_cnt': 'Fwd PSH Flags',
'fPktsPerSecond': 'Fwd Packets/s',
'bPktsPerSecond': 'Bwd Packets/s',
'min_flowpktl': 'Min Packet Length',
'max_flowpktl': 'Max Packet Length',
'mean_flowpktl': 'Mean Packet Length',
'std_flowpktl': 'Packet Length Std',
'var_flowpktl': 'Packet Length Variance',
'flow_fin': 'FIN Flag Count',
'flow_syn': 'SYN Flag Count',
'flow_rst': 'RST Flag Count',
'flow_psh': 'PSH Flag Count',
'flow_ack': 'ACK Flag Count',
'avgPacketSize': 'Average Packet Size',
'fAvgSegmentSize': 'Avg Fwd Segment Size',
'bAvgSegmentSize': 'Avg Bwd Segment Size',
'fSubFlowAvgPkts': 'Subflow Fwd Packets',
'fSubFlowAvgBytes': 'Subflow Fwd Bytes',
'bSubFlowAvgPkts': 'Subflow Bwd Packets',
'bSubFlowAvgBytes': 'Subflow Bwd Bytes',
'fInitWinSize': 'Init_Win_bytes_forward',
'bInitWinSize': 'Init_Win_bytes_backward',
'fDataPkts': 'act_data_pkt_fwd',
'fHeaderSizeMin': 'Min Header size_forward',
'label': 'Label',
'total_fhlen': 'Fwd Header Length',
'flow_urg': 'URG Flag Count'
}
# call rename () method
df.rename(columns=self.dict,
inplace=True)
return df
def r_hdf(self):
filename = 'prediction_data/data.h5'
df = pd.read_hdf(filename)
return df
def save_to_hdf(self):
# converting df(csv) to df(HDF5)
filename = 'prediction_data/Normalized-data.h5'
self.df.to_hdf(filename, 'data', mode='w', format='table')
rc.log("\n[cyan]Converted Df to HDF5[/]\n")
# del df
def df_info(self):
# df.head(5)
rc.log("\n[purple]Head of Dataframe: {}[/] \n".format(self.df.head(5)))
# rc.log(self.df.head(5))
# df.shape
rc.log("\n[magenta]Shape of Dataframe: {}[/]\n".format(self.df.shape))
# rc.log(self.df.shape)
# No. of rows and columns in dataframe
rc.log(
"\n[cyan]Number of Rows in Dataframe: {}[/]\n".format(self.df.shape[0]))
rc.log("\n[good]Number of Columns in Dataframe: {}[/]\n".format(
self.df.shape[1]))
# # df.info
# rc.log("\nDataframe Information: \n")
# self.df.info()
def columns_in_df(self):
rc.log("\n[yellow]Columns in Dataframe:[/] \n")
col = []
for i in self.df.columns:
col.append(i)
return col
def dropna(self):
# df.replace([np.inf, -np.inf], np.nan).dropna(axis=1)
self.df.replace([np.inf, -np.inf], np.nan, inplace=True)
# Dropping all the rows with nan valuess
self.df.dropna(inplace=True)
# -------------------changing datatypes
def check_size_dtypes(self, df):
max = df.max()
rc.log('[yellow]Maximum: {}[/]'.format(max))
# rc.log(max, 'max')
min = df.min()
rc.log('[blue]Minimum: {}[/]'.format(min))
# rc.log(min, 'min')
# rc.log(df.value_counts())
var1 = df.memory_usage(index=False, deep=True)
rc.log('[magenta]This is the memory usage: {}[/]'.format(var1))
# rc.log(var1, 'This is the memory usage')
# rc.log(df.sample(8))
def convert_datatypes(self, df, a='uint8'):
# rc.log('Trying to convert datatypes for less memory usage')
max = df.max()
rc.log('[yellow]Maximum: {}[/]'.format(max))
min = df.min()
rc.log('[blue]Minimum: {}[/]'.format(min))
# rc.log(df.value_counts())
var1 = df.memory_usage(index=False, deep=True)
rc.log('[cyan]This is the memory usage: {}[/]'.format(var1))
df = df.astype(a, errors='ignore')
var2 = df.memory_usage(index=False, deep=True)
# rc.log(var2, ' new memory usage| the difference -> ', var1 / var2)
return df
def normalize(self, df):
rc.log("[blue][* ] - Normalized data[/]")
normalized_df = ((df - df.min()) /
(df.max() - df.min())) * 225
return normalized_df
def apply_fn(self):
df['dst_port'] = d.normalize(df['dst_port'])
df['dst_port'] = d.convert_datatypes(df['dst_port'])
d.check_size_dtypes(df['dst_port'])
df['duration'] = d.normalize(df['duration'])
df['duration'] = d.convert_datatypes(df['duration'])
d.check_size_dtypes(df['duration'])
df['total_fpackets'] = d.normalize(df['total_fpackets'])
df['total_fpackets'] = d.convert_datatypes(df['total_fpackets'])
d.check_size_dtypes(df['total_fpackets'])
df['total_bpackets'] = d.normalize(df['total_bpackets'])
df['total_bpackets'] = d.convert_datatypes(df['total_bpackets'])
d.check_size_dtypes(df['total_bpackets'])
df['total_fpktl'] = d.normalize(df['total_fpktl'])
df['total_fpktl'] = d.convert_datatypes(df['total_fpktl'])
d.check_size_dtypes(df['total_fpktl'])
df['min_fpktl'] = d.normalize(df['min_fpktl'])
df['min_fpktl'] = d.convert_datatypes(df['min_fpktl'])
d.check_size_dtypes(df['min_fpktl'])
df['max_fpktl'] = d.normalize(df['max_fpktl'])
df['max_fpktl'] = d.convert_datatypes(df['max_fpktl'])
d.check_size_dtypes(df['max_fpktl'])
df['mean_fpktl'] = d.normalize(df['mean_fpktl'])
df['mean_fpktl'] = d.convert_datatypes(df['mean_fpktl'])
d.check_size_dtypes(df['mean_fpktl'])
df['std_fpktl'] = d.normalize(df['std_fpktl'])
df['std_fpktl'] = d.convert_datatypes(df['std_fpktl'])
d.check_size_dtypes(df['std_fpktl'])
df['min_bpktl'] = d.normalize(df['min_bpktl'])
df['min_bpktl'] = d.convert_datatypes(df['min_bpktl'])
d.check_size_dtypes(df['min_bpktl'])
df['max_bpktl'] = d.normalize(df['max_bpktl'])
df['max_bpktl'] = d.convert_datatypes(df['max_bpktl'])
d.check_size_dtypes(df['max_bpktl'])
df['std_bpktl'] = d.normalize(df['std_bpktl'])
df['std_bpktl'] = d.convert_datatypes(df['std_bpktl'])
d.check_size_dtypes(df['std_bpktl'])
df['flowBytesPerSecond'] = d.normalize(df['flowBytesPerSecond'])
df['flowBytesPerSecond'] = d.convert_datatypes(
df['flowBytesPerSecond'])
d.check_size_dtypes(df['flowBytesPerSecond'])
df['flowPktsPerSecond'] = d.normalize(df['flowPktsPerSecond'])
df['flowPktsPerSecond'] = d.convert_datatypes(
df['flowPktsPerSecond'])
d.check_size_dtypes(df['flowPktsPerSecond'])
df['mean_flowiat'] = d.normalize(df['mean_flowiat'])
df['mean_flowiat'] = d.convert_datatypes(df['mean_flowiat'])
d.check_size_dtypes(df['mean_flowiat'])
df['std_flowiat'] = d.normalize(df['std_flowiat'])
df['std_flowiat'] = d.convert_datatypes(df['std_flowiat'])
d.check_size_dtypes(df['std_flowiat'])
df['max_flowiat'] = d.normalize(df['max_flowiat'])
df['max_flowiat'] = d.convert_datatypes(df['max_flowiat'])
d.check_size_dtypes(df['max_flowiat'])
df['min_flowiat'] = d.normalize(df['min_flowiat'])
df['min_flowiat'] = d.convert_datatypes(df['min_flowiat'])
d.check_size_dtypes(df['min_flowiat'])
df['total_fiat'] = d.normalize(df['total_fiat'])
df['total_fiat'] = d.convert_datatypes(df['total_fiat'])
d.check_size_dtypes(df['total_fiat'])
df['mean_fiat'] = d.normalize(df['mean_fiat'])
df['mean_fiat'] = d.convert_datatypes(df['mean_fiat'])
d.check_size_dtypes(df['mean_fiat'])
df['std_fiat'] = d.normalize(df['std_fiat'])
df['std_fiat'] = d.convert_datatypes(df['std_fiat'])
d.check_size_dtypes(df['std_fiat'])
df['max_fiat'] = d.normalize(df['max_fiat'])
df['max_fiat'] = d.convert_datatypes(df['max_fiat'])
d.check_size_dtypes(df['max_fiat'])
df['min_fiat'] = d.normalize(df['min_fiat'])
df['min_fiat'] = d.convert_datatypes(df['min_fiat'])
d.check_size_dtypes(df['min_fiat'])
df['total_biat'] = d.normalize(df['total_biat'])
df['total_biat'] = d.convert_datatypes(df['total_biat'])
d.check_size_dtypes(df['total_biat'])
df['max_biat'] = d.normalize(df['max_biat'])
df['max_biat'] = d.convert_datatypes(df['max_biat'])
d.check_size_dtypes(df['max_biat'])
df['fpsh_cnt'] = d.normalize(df['fpsh_cnt'])
df['fpsh_cnt'] = d.convert_datatypes(df['fpsh_cnt'])
d.check_size_dtypes(df['fpsh_cnt'])
df['fPktsPerSecond'] = d.normalize(df['fPktsPerSecond'])
df['fPktsPerSecond'] = d.convert_datatypes(df['fPktsPerSecond'])
d.check_size_dtypes(df['fPktsPerSecond'])
df['bPktsPerSecond'] = d.normalize(df['bPktsPerSecond'])
df['bPktsPerSecond'] = d.convert_datatypes(df['bPktsPerSecond'])
d.check_size_dtypes(df['bPktsPerSecond'])
df['min_flowpktl'] = d.normalize(df['min_flowpktl'])
df['min_flowpktl'] = d.convert_datatypes(df['min_flowpktl'])
d.check_size_dtypes(df['min_flowpktl'])
df['max_flowpktl'] = d.normalize(df['max_flowpktl'])
df['max_flowpktl'] = d.convert_datatypes(df['max_flowpktl'])
d.check_size_dtypes(df['max_flowpktl'])
df['mean_flowpktl'] = d.normalize(df['mean_flowpktl'])
df['mean_flowpktl'] = d.convert_datatypes(df['mean_flowpktl'])
d.check_size_dtypes(df['mean_flowpktl'])
df['std_flowpktl'] = d.normalize(df['std_flowpktl'])
df['std_flowpktl'] = d.convert_datatypes(df['std_flowpktl'])
d.check_size_dtypes(df['std_flowpktl'])
df['var_flowpktl'] = d.normalize(df['var_flowpktl'])
df['var_flowpktl'] = d.convert_datatypes(df['var_flowpktl'])
d.check_size_dtypes(df['var_flowpktl'])
df['flow_fin'] = d.normalize(df['flow_fin'])
df['flow_fin'] = d.convert_datatypes(df['flow_fin'])
d.check_size_dtypes(df['flow_fin'])
df['flow_syn'] = d.normalize(df['flow_syn'])
df['flow_syn'] = d.convert_datatypes(df['flow_syn'])
d.check_size_dtypes(df['flow_syn'])
df['flow_rst'] = d.normalize(df['flow_rst'])
df['flow_rst'] = d.convert_datatypes(df['flow_rst'])
d.check_size_dtypes(df['flow_rst'])
df['flow_psh'] = d.normalize(df['flow_psh'])
df['flow_psh'] = d.convert_datatypes(df['flow_psh'])
d.check_size_dtypes(df['flow_psh'])
df['flow_ack'] = d.normalize(df['flow_ack'])
df['flow_ack'] = d.convert_datatypes(df['flow_ack'])
d.check_size_dtypes(df['flow_ack'])
df['avgPacketSize'] = d.normalize(df['avgPacketSize'])
df['avgPacketSize'] = d.convert_datatypes(df['avgPacketSize'])
d.check_size_dtypes(df['avgPacketSize'])
df['fAvgSegmentSize'] = d.normalize(df['fAvgSegmentSize'])
df['fAvgSegmentSize'] = d.convert_datatypes(df['fAvgSegmentSize'])
d.check_size_dtypes(df['fAvgSegmentSize'])
df['bAvgSegmentSize'] = d.normalize(df['bAvgSegmentSize'])
df['bAvgSegmentSize'] = d.convert_datatypes(df['bAvgSegmentSize'])
d.check_size_dtypes(df['bAvgSegmentSize'])
df['fSubFlowAvgPkts'] = d.normalize(df['fSubFlowAvgPkts'])
df['fSubFlowAvgPkts'] = d.convert_datatypes(df['fSubFlowAvgPkts'])
d.check_size_dtypes(df['fSubFlowAvgPkts'])
df['fSubFlowAvgBytes'] = d.normalize(df['fSubFlowAvgBytes'])
df['fSubFlowAvgBytes'] = d.convert_datatypes(df['fSubFlowAvgBytes'])
d.check_size_dtypes(df['fSubFlowAvgBytes'])
df['bSubFlowAvgPkts'] = d.normalize(df['bSubFlowAvgPkts'])
df['bSubFlowAvgPkts'] = d.convert_datatypes(df['bSubFlowAvgPkts'])
d.check_size_dtypes(df['bSubFlowAvgPkts'])
df['bSubFlowAvgBytes'] = d.normalize(df['bSubFlowAvgBytes'])
df['bSubFlowAvgBytes'] = d.convert_datatypes(df['bSubFlowAvgBytes'])
d.check_size_dtypes(df['bSubFlowAvgBytes'])
df['fInitWinSize'] = d.normalize(df['fInitWinSize'])
df['fInitWinSize'] = d.convert_datatypes(df['fInitWinSize'])
d.check_size_dtypes(df['fInitWinSize'])
df['bInitWinSize'] = d.normalize(df['bInitWinSize'])
df['bInitWinSize'] = d.convert_datatypes(df['bInitWinSize'])
d.check_size_dtypes(df['bInitWinSize'])
df['fDataPkts'] = d.normalize(df['fDataPkts'])
df['fDataPkts'] = d.convert_datatypes(df['fDataPkts'])
d.check_size_dtypes(df['fDataPkts'])
df['fHeaderSizeMin'] = d.normalize(df['fHeaderSizeMin'])
df['fHeaderSizeMin'] = d.convert_datatypes(df['fHeaderSizeMin'])
d.check_size_dtypes(df['fHeaderSizeMin'])
df['total_fhlen'] = d.normalize(df['total_fhlen'])
df['total_fhlen'] = d.convert_datatypes(df['total_fhlen'])
d.check_size_dtypes(df['total_fhlen'])
df['min_idle_s'] = d.normalize(df['min_idle_s'])
df['min_idle_s'] = d.convert_datatypes(df['min_idle_s'])
d.check_size_dtypes(df['min_idle_s'])
df['max_idle_s'] = d.normalize(df['max_idle_s'])
df['max_idle_s'] = d.convert_datatypes(df['max_idle_s'])
d.check_size_dtypes(df['max_idle_s'])
df['std_idle_s'] = d.normalize(df['std_idle_s'])
df['std_idle_s'] = d.convert_datatypes(df['std_idle_s'])
d.check_size_dtypes(df['std_idle_s'])
df['mean_idle_s'] = d.normalize(df['mean_idle_s'])
df['mean_idle_s'] = d.convert_datatypes(df['mean_idle_s'])
d.check_size_dtypes(df['mean_idle_s'])
df['flow_urg'] = d.normalize(df['flow_urg'])
df['flow_urg'] = d.convert_datatypes(df['flow_urg'])
d.check_size_dtypes(df['flow_urg'])
df['fHeaderSizeMin'] = d.normalize(df['fHeaderSizeMin'])
df['fHeaderSizeMin'] = d.convert_datatypes(df['fHeaderSizeMin'])
d.check_size_dtypes(df['fHeaderSizeMin'])
'''
39 URG Flag Count 87 non-null float64 'flow_urg'
26 Fwd Header Length 0 non-null float64 'fHeaderSizeMin'
Idle Mean 87 non-null float64 'mean_idle_s', 'std_idle_s', 'max_idle_s', 'min_idle_s'
52 Idle Std 87 non-null float64
53 Idle Max 87 non-null float64
54 Idle Min 87 non-null float64
'''
rc.log("[magenta]Data information: \n{}[/]".format(df.info()))
if __name__ == "__main__":
filename = 'csvs/merged_data.csv'
df = pd.read_csv(filename)
rc.log("[cyan][*_*] - Preprocessing the captured Data - [*_*][/]\n\n")
rc.log("[yellow]Columns/ Features in captured data: \n{}[/]".format(df.columns))
rc.log("[blue]Shape of captured data: \n{}[/]".format(df.shape))
d = Preprocessing(df)
d.dropna()
d.rm_col()
d.apply_fn()
l = d.col_rename()
col = []
for i in l:
# col.append(i)
rc.log("[cyan]<------- {} ------->[/]".format(i))
# l.info()
rc.log("[good][ DONE ] - File is ready to fed to ML/ DL model. [magenta][ *in HDF5 Format ][/][/]")
filename1 = 'prediction_data/normed_data.h5'
df.to_hdf(filename1, 'data', mode='w', format='table')
rc.log(df.columns)
rc.log("[bold blue][ *** ] - Shape of captured data: {}[/]".format(df.shape))
rc.save_html("norm-report.html")
# df = pd.read_csv(filename)
# rc.log("[cyan][*_*] - Preprocessing the captured Data - [*_*][/]\n\n")
# rc.log("[yellow]Columns/ Features in captured data: \n{}[/]".format(df.columns))
# # df.replace([np.inf, -np.inf], np.nan).dropna(axis=1)
# rc.log("[blue]Shape of captured data: \n{}[/]".format(df.shape))
# d = Preprocessing(df)
# df = d.r_csv(filename)
# rc.log(df)
# rc.log("[purple]Droping NaN values.....\n[/]")
# d.dropna()
# # # df.replace([np.inf, -np.inf], np.nan).dropna(axis=1)
# # df.replace([np.inf, -np.inf], np.nan, inplace=True)
# # # Dropping all the rows with nan valuess
# # df.dropna(inplace=True)
# rc.log("[bold bad]Removing unwanted columns...\n[/]")
# d.rm_col()
# d.apply_fn()
# rc.log("[good]Giving Columns a meaningful name...[/]")
# l = d.col_rename()
# rc.log("[cyan]Data info(): \n[/] \n")
# l.info()
# rc.log("[magenta][*_*] - Saving preprocessed_data.csv,..[/]")
# df.to_csv("preprocessed_csv/preprocessed_data.csv", encoding='utf-8')
# rc.log("\n[good][*_*] - Saved Preprocessed csv[/]\n")
# rc.log("[good][ DONE ] - File is ready to fed to ML/ DL model. [magenta][ *in HDF5 Format ][/][/]")
# d.save_to_hdf()
# rc.log(df.columns)
# rc.log("[bold blue][ *** ] - Shape of captured data: {}[/]".format(df.shape))
# rc.save_html("norm-report.html")