labs/Module-6/1.Botnet_Detection.ipynb

In [ ]:

import numpy as np

In [ ]:

import pandas as pd
from sklearn.linear_model import *
from sklearn.tree import *
from sklearn.naive_bayes import *
from sklearn.neighbors import *
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
%matplotlib inline

In [ ]:

dataset= pd.read_csv('datasets2/network-logs.csv')
samples = dataset.iloc[:, [1,2]].values
targets = dataset['ANOMALY'].values
training_samples, testing_samples, training_targets, testing_targets = train_test_split(samples, targets, test_size = 0.3, random_state = 0)

In [ ]:

#K-Nearest neighbors model
knc = KNeighborsClassifier(n_neighbors= 2)
knc.fit(training_samples, training_targets)
knc_prediction = knc.predict(testing_samples)
knc_accuracy = 100.0 * accuracy_score(testing_targets, knc_prediction)
print("K-Nearest Neighbours accuracy: " + str(knc_accuracy))

In [ ]:

from sklearn.metrics import classification_report , confusion_matrix

print("Confusion Matrix:")
print(confusion_matrix(testing_targets, knc_prediction))

print("\nClassification Report:")
print(classification_report(testing_targets, knc_prediction))

In [ ]:

# Decision tree model
dtc = DecisionTreeClassifier(random_state=0)
dtc.fit(training_samples, training_targets)
dtc_prediction = dtc.predict(testing_samples)
dtc_accuracy = 100.0 * accuracy_score(testing_targets, dtc_prediction)
print("Decision Tree Accuracy: " + str(dtc_accuracy))

In [ ]:

print("Confusion Matrix:")
print(confusion_matrix(testing_targets, dtc_prediction))

print("\nClassification Report:")
print(classification_report(testing_targets, dtc_prediction))

In [ ]:

# Gaussian Naive Bayes Model
gnb = GaussianNB()
gnb.fit(training_samples, training_targets)
gnb_prediction = gnb.predict(testing_samples)
gnb_accuracy = 100 * accuracy_score(testing_targets, gnb_prediction)
print("Gaussian Naive Bayes accuracy: " + str(gnb_accuracy))

In [ ]:

print("Confusion Matrix:")
print(confusion_matrix(testing_targets, gnb_prediction))

print("\nClassification Report:")
print(classification_report(testing_targets, gnb_prediction))

In [ ]: