GitHub - vatsalgupta13/Network-Intrusion-Detection-using-Machine-Learning: In this project, we have made a knowledge-based intrusion detection system which is also known as the anomaly-based system. It registers the anomalies and in future predicts such malicious network to send out an alert. This way, the network can disconnect to such a connection and then have only secured connections.

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In this project, we have made a knowledge-based intrusion detection system which is also known as the anomaly-based system. It registers the anomalies and in future predicts such malicious network to send out an alert. This way, the network can disconnect to such a connection and then have only secured connections.

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Computer Networks Lab Final Report - 171040- 60,38,70.pdf		Computer Networks Lab Final Report - 171040- 60,38,70.pdf
Network Intrusion Detection using Machine Learning.py		Network Intrusion Detection using Machine Learning.py
readme.txt		readme.txt

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NETWORK INTRUSION DETECTION USING MACHINE LEARNING

1) First install python 3.x along within a python IDE. It is encouraged to download spyder IDE since that's the IDE we have used to write and run the code
2) Download the dataset from https://www.unb.ca/cic/datasets/nsl.html. It is freely avalaible. In case of any issues from this website, download this dataset from Kaggle - https://www.kaggle.com/hassan06/nslkdd. [Dataset has also been provided in the 'Dataset' folder]
Note: For the purposes of our code: there is no need for csv files, txt files will work.
Only KKDtest.txt , KDDtrain.txt files are needed. 
3) Set the working directory as the location of the folder containing the dataset.
4) Install the required modules using pip installer on anaconda prompt(if spyder installed on anaconda environment) or else on terminal/cmd (if python has been installed separately).
5) Open the code file - Network Intrusion Detection using Machine Learning.py. Run the code at once or run the code in sections to understand the working of each part of the code. This code can also be run with the jupyter notebook.
6) The code is properly commented for better understanding.