80 lines
3.1 KiB
Python
80 lines
3.1 KiB
Python
import pandas as pd
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import statistics as stat
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import numpy as np
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import matplotlib.pyplot as plt
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import math as m
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dataset = pd.read_csv('csv/team13_protocol.csv').fillna(0)
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dataset['timestamp'] = pd.to_datetime(dataset['timestamp'], unit='s')
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dataset = dataset.set_index('timestamp')
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print(dataset)
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p6_pkts = dataset['6 · # Packets'].tolist()
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#return number of elements in p6_pkts list
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n = len(p6_pkts)
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#calculate median
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median = stat.median(p6_pkts)
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#in case of 0 values, replace it with the median
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p6_pkts = [ p6_pkts[i] if p6_pkts[i] !=0 else median for i in range(0,n) ]
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pkt_fft = np.fft.fft(p6_pkts) #calculates the fft
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pkt_amp = np.abs(pkt_fft) #returns absolute values
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k = range(0,n-1) #creates an array from 0 to n-1
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#set k values in the x-axis and specify the limit
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x = k[1:m.floor(n/2)]
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#set amp. values in the y-axis and specify the limit
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y = pkt_amp[1:m.floor(n/2)]
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plt.figure(figsize=(20,10))
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plt.tight_layout(h_pad=0.5)
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plt.subplot(2, 1, 1)
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plt.stem(x, y)
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#find max index and value
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max_k = np.flip(np.argsort(pkt_amp[1:m.floor(n/2)]))[0]
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max_amp = pkt_amp[1:m.floor(n/2)][max_k]
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plt.xlim(1, m.floor(n/2))
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plt.xlabel('k') #sets x-axis label
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#set y-axis label
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plt.ylabel('Amplitude [millions of pkts]')
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plt.title('Amp. Spectrum for #pkts') #displays title
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print('TCP #pkts/hour - FFT max value: ', round(max_amp / 1000000, 2))
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print('TCP #pkts/hour - k of FFT max value: ', np.where(pkt_amp == max_amp))
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print('TCP #pkts/hour - period of k corresponding to FFT max value: ', n / 5)
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p6_uIPs = dataset['6 · # Unique Source IPs'].tolist()
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#return number of elements in p6_pkts list
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n_2 = len(p6_uIPs)
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#calculate median
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median_2 = stat.median(p6_uIPs)
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#in case of 0 values, replace it with the median
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p6_uIPs = [ p6_uIPs[i] if p6_uIPs[i] !=0 else median_2 for i in range(0,n_2) ]
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uIPs_fft = np.fft.fft(p6_uIPs) #calculates the fft
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uIPs_amp = np.abs(uIPs_fft) #returns absolute values
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k = range(0,n_2-1) #creates an array from 0 to n-1
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#set k values in the x-axis and specify the limit
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x = k[1:m.floor(n_2/2)]
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#set amp. values in the y-axis and specify the limit
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y = uIPs_amp[1:m.floor(n/2)]
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plt.subplot(2, 1, 2)
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plt.stem(x, y)
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#find max index and value
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max_k = np.flip(np.argsort(uIPs_amp[1:m.floor(n_2/2)]))[0]
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max_amp = uIPs_amp[1:m.floor(n_2/2)][max_k]
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plt.xlim(1, m.floor(n_2/2))
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plt.xlabel('k') #sets x-axis label
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#set y-axis label
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plt.ylabel('Amplitude [millions of unique source IPs]')
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plt.title('Amp. Spectrum for #unique source IPs') #displays title
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plt.show()
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print('TCP #uIPs/hour - FFT max value: ', round(max_amp / 1000000, 2))
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print('TCP #uIPs/hour - k of FFT max value: ', np.where(uIPs_amp == max_amp))
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print('TCP #uIPs/hour - period of k corresponding to FFT max value: ', n_2 / 31)
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# Plot TCP #pkts/hour and TCP #uIPs/hour timeseries
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plt.figure(figsize=(20,10))
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plt.tight_layout(h_pad=2)
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plt.subplot(2, 1, 1)
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dataset['6 · # Packets'].plot(xlabel='', ylabel='Number of TCP Packets', title='Number of TCP Packets over Time')
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plt.subplot(2, 1, 2)
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dataset['6 · # Unique Source IPs'].plot(xlabel='Time', ylabel='Number of Unique TCP Source IPs', title='Number of Unique TCP Source IPs over Time')
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plt.show()
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