NTP Model code

NTP Amplification Attack/ntpModel.py

+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jun 22 19:14:10 2020
+
+@author: Sakindu Udagedara
+"""
+
+
+#Importing the libraries 
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+#Importing tha dataset
+dataset = pd.read_csv('Sample.csv')
+
+X = dataset.iloc[:,[1,3]].values 
+Y = dataset.iloc[:,5].values 
+
+#taking care of missing data
+#from sklearn.preprocessing import Imputor
+#imputor = Imputor (missing_values = 'NaN', strategy = 'mean' , axis = 0)
+#imputor.fix(X[:,])
+
+#encoding catagarical data
+from sklearn.preprocessing import LabelEncoder
+lebekencoder_X = LabelEncoder()
+#X[:, 0] = lebekencoder_X.fit_transform(X[:, 0])
+#X[:, 2] = lebekencoder_X.fit_transform(X[:, 2])
+lebekencoder_Y = LabelEncoder()
+Y = lebekencoder_Y.fit_transform(Y)
+
+#spliting data into tarin set and test set 
+from sklearn.model_selection import train_test_split
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.3, random_state = 0)
+
+#feature scaling
+from sklearn.preprocessing import StandardScaler
+sc_X = StandardScaler()
+X_train = sc_X.fit_transform(X_train)
+X_test = sc_X.fit_transform(X_test) 
+
+# Fiting SVM to the Traing set
+from sklearn.svm import SVC
+classifier = SVC(kernel = 'linear', random_state = 0)
+classifier.fit(X_train, Y_train)
+
+# Predicting the Test set results
+y_pred = classifier.predict(X_test)
+
+#makeing the confusion matrix
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(Y_test, y_pred)
+
+#Visualizing the Training set results
+from matplotlib.colors import ListedColormap
+X_set, Y_set = X_train, Y_train
+X1, X2 = np.meshgrid(np.arange(start = X_set[:,0].min() - 1, stop = X_set[:,0].max() + 1, step = 0.01),
+                     np.arange(start = X_set[:,1].min() - 1, stop = X_set[:,1].max() + 1, step = 0.01))
+plt.contour(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
+            alpha = 0.75, cmap = ListedColormap(('red', 'green')))
+plt.xlim(X1.min(),X2.max())
+plt.ylim(X2.min(),X1.max())
+for i,j in enumerate(np.unique(Y_set)):
+    plt.scatter(X_set[Y_set == j, 0], X_set[Y_set == j, 1],
+                c = ListedColormap(('red','green'))(i),label = j)
+plt.title('SVM (Training set)' )
+plt.xlabel('Source Port')
+plt.ylabel('Destination Port')
+plt.legend()
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+