tamerthamoqa/cic-ids-2018-intrusion-detection-classification
Baseline experiments on training a Decision Tree Classifier and a Random Forest Classifier using Grid Search with Cross Validation on the CIC IDS 2018 dataset for training Machine Learning network intrusion detection classifier models.
This project helps network security professionals evaluate machine learning models for detecting network intrusions. It takes raw network traffic data, processes it, and then trains and evaluates machine learning classifiers to identify malicious activities like Bot and DoS attacks. The output is a clear performance report on how well different models distinguish between benign and malicious network behavior, helping you choose the best detection strategy.
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Use this if you are a network security analyst or researcher looking to benchmark or understand the effectiveness of different machine learning models for real-time intrusion detection using established network traffic datasets.
Not ideal if you need a fully deployed, production-ready intrusion detection system, as this project focuses on baseline experimentation and model evaluation.
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Jun 03, 2022
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