Delete slowlorisModel.py

WANHEDA Server/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()
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