Merge branch 'Navindu' into 'master'

Navindu

See merge request !7

Slowloris Attack/slowlorisModel.py

+# -*- coding: utf-8 -*-
+"""Navindu.ipynb"""
+
+# Importing the libraries
+
+# Fundamental package for scientific computing
+import numpy as np
+# Plotting library
+import matplotlib.pyplot as plt
+# Data manipulation and analysis library
+import pandas as pd
+# Completing missing values
+from sklearn.impute import SimpleImputer
+# Encode target labels with value between 0 and n_classes-1
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+# Applies transformers to columns of an array or pandas DataFrame
+from sklearn.compose import ColumnTransformer
+# Split arrays or matrices into random train and test subsets
+from sklearn.model_selection import train_test_split
+# Standardize features by removing the mean and scaling to unit variance
+from sklearn.preprocessing import StandardScaler
+# Logistic Regression classifier
+from sklearn.linear_model import LogisticRegression
+# Compute confusion matrix to evaluate the accuracy of a classification
+from sklearn.metrics import confusion_matrix
+# Accuracy classification score
+from sklearn.metrics import accuracy_score
+# Build a text report showing the main classification metrics
+from sklearn.metrics import classification_report
+# Data visualization library based on matplotlib
+import seaborn as sns
+
+# Importing the dataset
+dataset = pd.read_csv('/Users/harithachanuka/Documents/SLIIT/Research/Harry/Wanheda_Server/Slowloris/SlowlorisDATASET.numbers')
+
+# Read in data and display first 5 rows
+dataset.head()
+
+# Drop the dataset
+dataset = dataset.dropna()
+
+#Data cleaning
+dataset.isnull().sum()
+
+#Encoding categarical data
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+labelencoder_Y = LabelEncoder()
+dataset.iloc[:, 78] = labelencoder_Y.fit_transform(dataset.iloc[:, 78])
+
+dataset.shape
+
+dataset = dataset[~dataset.isin([np.isnan]).any(1)].astype(np.float64)
+
+dataset = dataset[~dataset.isin([np.isinf]).any(1)].astype(np.float64)
+
+dataset = dataset.mask(np.isinf(dataset))
+
+dataset = dataset.dropna()
+
+dataset.shape
+
+#returns a boolean array of the X
+np.any(np.isinf(dataset))
+
+#detect numpy arrays of different data types
+np.any(np.isnan(dataset))
+
+#get all colomns without last colomn in X axis and get last colomn in y axis 
+X = dataset.iloc[:, :-1].values
+y = dataset.iloc[:, 78].values
+
+dataset.info()
+
+y
+
+df = pd.DataFrame(data=X)
+
+df
+
+#Spliting the dataset into the training 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 = 0.2, 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.transform(X_test)
+
+y_train.shape
+
+#fitting simple linear regression to the training set
+
+from sklearn.linear_model import LinearRegression
+regressor = LinearRegression()
+regressor.fit(X_train, y_train)
+
+#predecting the data
+
+y_pred = regressor.predict(X_test)
+print(y_pred)
+
+X_train.shape
+
+y_train.shape
+
+#visual plot
+
+plt.scatter(X_train, y_train, color = 'red')
+plt.plot(X_train, y_train, color = 'red')
+plt.plot(X_test, y_pred, color = 'blue')
+plt.title('Slowloris Attack')
+plt.xlabel('Flow Duration')
+plt.ylabel('Total Length')
+plt.show()
\ No newline at end of file

Slowloris.py

+# -*- coding: utf-8 -*-
+"""
+Created on Sat May 16 17:40:32 2020
+
+@author: navin
+"""
+
+
+# Simple Linear Regression
+
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('dataset_7.csv')
+X = dataset.iloc[:, :-1].values
+y = dataset.iloc[:, 84].values
+
+# Splitting the dataset into the Training 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 = 1/3, random_state = 0)
+
+# Training the Simple Linear Regression model on the Training set
+from sklearn.linear_model import LinearRegression
+regressor = LinearRegression()
+regressor.fit(X_train, y_train)
+
+# Predicting the Test set results
+y_pred = regressor.predict(X_test)
+
+# Visualising the Training set results
+plt.scatter(X_train, y_train, color = 'red')
+plt.plot(X_train, regressor.predict(X_train), color = 'blue')
+plt.title('Slowloris Attack Testing')
+plt.xlabel('X')
+plt.ylabel('Y')
+plt.show()
+
+# Visualising the Test set results
+plt.scatter(X_test, y_test, color = 'red')
+plt.plot(X_train, regressor.predict(X_train), color = 'blue')
+plt.title('Slowloris Attack Testing')
+plt.xlabel('X')
+plt.ylabel('Y')
+plt.show()
+