noushinpervez/Intrusion-Detection-CICIDS2017
This repository contains an in-depth analysis of the Intrusion Detection Evaluation Dataset (CIC-IDS2017) for Intrusion Detection, showcasing the implementation and comparison of different machine learning models for binary and multi-class classification tasks.
This project helps network security analysts evaluate and compare different machine learning models for detecting cyber intrusions. It takes network traffic data, including normal activity and various attack types, and outputs a comparison of model performance metrics to help identify the most effective intrusion detection system. Network security engineers or analysts would use this to enhance their understanding and selection of IDS solutions.
111 stars. No commits in the last 6 months.
Use this if you need to understand how different machine learning models perform in identifying various cyberattacks within network traffic, using a real-world dataset.
Not ideal if you are looking for an out-of-the-box, deployable intrusion detection system rather than an analytical comparison of model efficacy.
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Oct 19, 2023
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