Network-Intrusion-Detection-Using-Machine-Learning-Techniques and NSL-KDD-Network-Intrusion-Detection
Both tools are competitors, as they both offer implementations of various machine learning algorithms for network intrusion detection, with the choice likely depending on the specific algorithms or dataset handling preferred by the user.
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 NSL-KDD-Network-Intrusion-Detection
Mamcose/NSL-KDD-Network-Intrusion-Detection
Machine Learning Algorithms on NSL-KDD dataset
This project helps network security professionals identify cyberattacks by analyzing network traffic data. It takes raw network connection logs as input and outputs classifications of whether a connection is normal or an intrusion, helping to flag suspicious activity. This is intended for network administrators, security analysts, or anyone responsible for maintaining network integrity.
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