Intrusion-Detection-System-Using-Machine-Learning and IoT-Network-Intrusion-Detection-System-UNSW-NB15
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 IoT-Network-Intrusion-Detection-System-UNSW-NB15
abhinav-bhardwaj/IoT-Network-Intrusion-Detection-System-UNSW-NB15
Network Intrusion Detection based on various machine learning and deep learning algorithms using UNSW-NB15 Dataset
This project helps operations engineers or cybersecurity analysts monitor IoT network traffic to detect and classify cyberattacks. It takes raw network data from an IoT environment, processes it, and then identifies if traffic is normal or abnormal. If abnormal, it further categorizes the specific type of attack (e.g., Denial of Service, Exploits).
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