Question

Python

Task description: Cyber security attacks can be grouped into 4 categories: DOS attack, PROBE attack, R21 attack, and U2R attack. There are 11 different kinds of attacks in DOS, 7 kinds of attacks in PROBE, 15 kinds of attacks in R21, and 8 kinds of attacks in U2R. You are given the statistical attack data saved in file 'attack-type-frequency.csv' Attack type apache2 back category number of attack dos dos dos dos u2r 124 432 43 96 482 2625 954 679 Frequency % 0.52 1.8 0.18 0.4 2.01 10.93 3.97 2.83 udpstorm buffer overflow u2r erl rootkit u2r u2r (The above data is for demonstration purposes only. Please download the full version of attack type-frequency.csv.) You are asked to read the file data and visualize the data using matplotlib's scatter plot with the following settings figsize (10,5), dpi-100 scatter size is changed based on the value of "number of attack" . X axis is number of attack data with label: Attack number detected . Y axis is frequency data with label: Attack frequency Scatter displays all the 4 attack categories in one plot: "yellow" for DOS; "green" for U2R; "blue" for R21; and "red" for PROBE. . legend(markerscale-0.3) or any value you think proper Sample output as shown in the following figure is for demonstration purposes only. ● DOS R21 PROBE 0 500 1000 1500 2000 2500 Attack number detected

Answer 1

Hello there, please make sure you have pandas, matplotlib and sklearn installed on your system.

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import pandas as pd
import matplotlib.pyplot as plt

from sklearn.preprocessing import normalize

def freq_scatter_plot(filename):
   df = pd.read_csv(filename)
   columns = list(df.columns)
   ### x, y
   x = list(range(len(df.index)))
   y = df[columns[-1]].values

   ## size
   marker_size = df[columns[2]].values

   ## color
   colors = {'dos':'y', 'u2r':'g', 'r21':'b', 'probe':'r'}
   attack_type = list(df[columns[1]].values)

   ## set plot
   fig = plt.figure(figsize=(10, 5), dpi=100)
   ax = fig.add_subplot(1, 1, 1)
   for i in range(len(df.index)):
       ax.scatter(x[i], y[i], s=marker_size[i], c=colors[attack_type[i]])

   ax.set_xlabel('Attack number detected')
   ax.set_ylabel('Attack frequency')
   plt.legend(markerscale=0.3)
   plt.show()

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