sayanbanerjee992/Network-based-Intrusion-Detection-System
The case study on a Network-based Intrusion Detection System is a Machine Learning-based Web application based on https://arxiv.org/pdf/1903.02460.pdf research paper. I have performed both binary and multi-class classification to predict the presence of any intrusion-based signal. If present, then which type of signal is present in the network. I have used various techniques like under sampling and over sampling because of the imbalances in the dataset, then train the model using Machine Learning techniques like Logistic Regression, Random Forest, XGBoost. Lastly built a web application and deploy it to AWS using AWS EC2 instance
This system helps network administrators and security analysts automatically detect and classify cyber intrusions in network traffic. It takes raw network data as input and identifies whether an intrusion is present and, if so, what type of attack it is. This helps security teams quickly identify and respond to threats.
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Use this if you need an automated way to monitor network activity for potential cyberattacks and categorize detected threats.
Not ideal if you require a highly customized intrusion detection system or need to integrate with specific, non-standard network monitoring tools.
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Apr 03, 2022
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