Network-Intrusion-Detection-Using-Machine-Learning-Techniques and Network-Intrusion-Detection
These are competitors offering alternative implementations of network intrusion detection using machine learning on similar datasets, where one (B) focuses on the NSL-KDD dataset while the other (A) demonstrates multiple classical ML algorithms (SVM, Decision Tree, KNN, etc.) for the same classification task.
About Network-Intrusion-Detection-Using-Machine-Learning-Techniques
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.
About Network-Intrusion-Detection
CynthiaKoopman/Network-Intrusion-Detection
Machine Learning with the NSL-KDD dataset for Network Intrusion Detection
This project helps network security analysts evaluate the effectiveness of different machine learning models in identifying network intrusions. By inputting network traffic data, it generates analyses to show how well methods like Decision Trees and Random Forests can detect suspicious activity. It's designed for cybersecurity professionals responsible for safeguarding network infrastructure.
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