ANN-for-DDoS-detection

Prerequisites

Download & Install Python 3.6 64-bit https://www.python.org/downloads/ (Make sure you add it to PATH)

Download & install Wireshark (Make sure Tshark and WinPcap are installed aswell)

Download & Install https://landinghub.visualstudio.com/visual-cpp-build-tools (Microsoft Visual C++ Redistributable for Visual Studio 2017)

Installation

Clone this repository to desired location

Open CMD

'cd' to repository location

use the following line in CMD to install the necessary requirements "pip install -r requirements.txt"

Starting Off

Open CMD

'cd' to repository location

python ProjectANN.py

Input number between 1-7, depending on what you would like to do

1 - Packet Sniffer

This is a basic packet sniffer

From the list, input the name of the inetrface you wish to sniff Packet information should now be seen

ctrl + c to cancel

2 - ANN Data Gatherer

This is used to create a dataset to train and test an ANN.

From the list, input the name of the inetrface you wish to gather data from

ctrl + c to cancel

3 - Neural Network Trainer

Creates and trains an ANN from a dataset

input name of the CSV dataset file you wish to use If you want to load a previous model, input 'y' and then input the name of the model Else just hit Enter Depending on model topology and the size of the dataset, the process may take a while Once finished, input 'y' to see the Weights and intercepts of the model after training input 'y' again to save the model (Must end in '.sav')

4 - Data Viewer

Allows for viewing the data within a dataset

input name of CSV Dataset you wish to view input 'a' to see All, 'n' to see just the numerical data, 'c' to see just categorical data

5 - Live Neural Network

Uses a trained ANN to detect DDoS Attacks

From the list, input the name of the inetrface you wish to detect DDoS attacks on input the filename of a trained model This will continuously run until either stopped or an attack is detected

6 - Visual ANN

Shows a visual representation of what an ANN model looks like (Can be changed in code, currently displays an input layer of 8, 2 hidden layers of 100 and an output layer of 1.

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