# ddos

**Repository Path**: glintfreedom/ddos

## Basic Information

- **Project Name**: ddos
- **Description**: No description available
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-06-22
- **Last Updated**: 2024-06-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Machine and Deep Learning for DDoS Detection
### Marcos V. O. Assis (mvoassis@gmail.com)
***

> ## Published Results:

* *A GRU deep learning system against attacks in software defined networks*

* https://doi.org/10.1016/j.jnca.2020.102942



* \***Update - 06/2022** - improved detection results through better data cleaning process. Updated results on Git. 

> ## Objectives

1. Evaluate different Machine and Deep Learning methods for anomaly detection.
2. Detection of Distributed Denial of Service Attacks

> ## Dataset

* CIC-DDoS2019 - https://www.unb.ca/cic/datasets/ddos-2019.html

> ## Evaluated Methods

* Gated Recurrent Units (GRU)
* Long-Short Term Memory (LSTM)
* Convolutional Neural Network (CNN)
* Deep Neural Network (DNN)
* Support Vector Machine (SVM)
* Logistic Regression (LR)
* Gradient Descent (GD)
* k Nearest Neighbors (kNN)

> ## Environment Config.

* Python 3.7.13
* Numpy 1.16.4
* Scikit-learn 0.21.2
* Pandas 0.24.2
* Tensorflow 1.14.0
* Keras 2.2.4
* Matplotlib 3.1.0
* Seaborn 0.11.2

***