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https://github.com/arjunraj79/custom_nids_with_ml

A simple Python script to check the strength of a password based on length, the inclusion of numbers, special characters, and upper/lower case letters.
https://github.com/arjunraj79/custom_nids_with_ml

datapreprocessing dos-attack feature-extraction intrusion-detection malware-analysis malware-detection ml-engineering modeltraining networktrafficanalysis portscanning realtime-detection virtualbox

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A simple Python script to check the strength of a password based on length, the inclusion of numbers, special characters, and upper/lower case letters.

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# Custom Network Intrusion Detection System (NIDS)



## Project Overview

This project aims to create a custom Network Intrusion Detection System (NIDS) that can detect various types of network attacks, such as Denial of Service (DoS), port scanning, and unauthorized access, by analyzing network traffic data. The system uses a machine learning model trained on a dataset of labeled network traffic to identify suspicious patterns.



## Installation



### Steps to Set Up the Project Locally



1. Clone the repository and navigate to the project directory.

2. Create a virtual environment:

   ```bash

   python -m venv venv

## Activate it:



bash

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.\venv\Scripts\activate

## Install the required Python libraries using the requirements.txt file:



bash

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pip install -r requirements.txt

## If you encounter activation issues, use PowerShell to give access with the following commands:



powershell

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Set-ExecutionPolicy -ExecutionPolicy RemoteSigned

powershell

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Unblock-File V:\Github\Custom_NIDS_with_ML.\venv\Scripts\Activate.ps1

powershell

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Invoke-Command -ScriptBlock {V:\Github\Custom_NIDS_with_ML.\venv\Scripts\activate}

powershell

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V:\Github\Custom_NIDS_with_ML.\venv\Scripts\activate.bat

Add a database CSV file to your project.



## Setting Up the Dataset

To get started, you'll need to download the dataset and place it in a data/ folder within your project directory. Follow these steps:



### For the NSL-KDD dataset:



Download the NSL-KDD dataset from the Kaggle website.

Extract the downloaded zip file to a folder named data within your project directory.

Rename the dataset file to NSL-KDD.csv for simplicity.



### For the CICIDS2017 dataset:



Download the CICIDS2017 dataset from the Canadian Institute for Cybersecurity website.

Extract the downloaded zip file to a folder named data within your project directory.

Rename the dataset file to CICIDS2017.csv for simplicity.

Once you've downloaded and placed the dataset in the data/ folder, you can modify the load_data function to load the dataset correctly.



## Model Details

Details about the machine learning model, including the training process and evaluation metrics, will be documented here.



## Limitations

Any known limitations or potential future improvements can be noted in this section.



Note: VS Code needs to run in admin mode for some features to function correctly.



## Real-Time Intrusion Detection

### Network Traffic Capture

scapy requires access to network interfaces, which might require administrative privileges.

Ensure no other process is using the network interface that could interfere with packet capture.

### Simulating Network Traffic

You can use tools like nmap to generate traffic for testing your NIDS.

For example, in a separate terminal window, you can run:

nmap -sS 

This will simulate a SYN scan, and your NIDS should detect it as a potential intrusion.

### Thank you for using this NIDS project!