Intrusion-Detection-System-Using-Machine-Learning and Network-Intrusion-Detection-Using-Machine-Learning-Techniques
About Intrusion-Detection-System-Using-Machine-Learning
Western-OC2-Lab/Intrusion-Detection-System-Using-Machine-Learning
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
This project helps cybersecurity professionals and network engineers identify cyber-attacks in connected vehicle networks. It takes network traffic data as input and outputs classifications of known or unknown intrusion attempts. The end-user persona is likely a security analyst or an operations engineer responsible for securing Internet of Vehicles (IoV) infrastructure.
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.
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