dimtics/Network-Intrusion-Detection-Using-Machine-Learning-Techniques
Network intrusions classification using algorithms such as Support Vector Machine (SVM), Decision Tree, Naive Baye, K-Nearest Neighbor (KNN), Logistic Regression and Random Forest.
This project helps network security analysts automatically classify different types of network intrusions to protect systems more effectively. It takes in raw network traffic data and outputs a classification of the intrusion type, such as DoS or probing, helping security teams quickly identify and respond to threats. This is designed for network defenders and security operations center (SOC) personnel.
102 stars. No commits in the last 6 months.
Use this if you need an automated system to analyze network traffic and categorize potential security threats without manual inspection.
Not ideal if you need to detect highly novel or zero-day attacks that do not fit predefined intrusion patterns.
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Jupyter Notebook
License
MIT
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Last pushed
Sep 27, 2017
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