Course Project | DDOS Detection by naive bayes

The project aims to apply one machine learning (ML) technique to solve an issue in any field.

Issue description

Distributed Denial of service (DDoS), attacks users from accessing the network and makes services unavailable or only partially available. The attacker floods the targeted machines or resources with excessive requests. The victim's incoming traffic originates from many different sources. Attackers use many (remotely) controlled computers to attack the victim.

Machine learning (ML) technique

In this project, we use the Naive Bayes technique to detect which if the request ddos or benign.

Dataset

The Dataset is extracted from different IDS datasets that were produced in different years and different experimental DDoS traffic generation tools, it has more than 12 milion records (ddos and benign) and 85 features.

Tools

Instructions to run notebook

Required packages:

ipaddress

pip install ipaddress

seaborn

pip install seaborn

pandas

pip install pandas

matplotlib

pip install matplotlib

sklearn

pip install -U scikit-learn

After downloading "ProjectCode.zip" and packages, you have two options of environments to run:

Running on local jupyter notebook

In this option, you must have a large space to import and build a model according to a large dataset.

Running on cloud platform

We use this option by using azure services in Machine Learning, is a cloud-based solution for ML workload and provides an end-to-end machine learning platform to enable users to build and deploy models faster on Azure. First, create a notebook for the project. Second, create a compute instance to execute the written code. The instance we chose had the following specifications Standard_E4a_v4 (4 cores, 32 GB RAM, 100 GB disk)

Prepared By

• Shoug Ali Alsuhaibani ssuhaibani@sm.imamu.edu.sa
• Sarah Khalid Alaradi skmaloridi@sm.imamu.edu.sa