IoT-Network-Intrusion-Detection-System-UNSW-NB15 and Network-Intrusion-Detection
These are competitors—both implement machine learning-based network intrusion detection systems on overlapping datasets (particularly UNSW-NB15), with B offering broader dataset coverage (KDDCup '99 and NSL-KDD in addition) and significantly higher adoption (762 vs 197 stars), making it the more comprehensive alternative.
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).
About Network-Intrusion-Detection
vinayakumarr/Network-Intrusion-Detection
Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15
This project helps cybersecurity analysts and network administrators detect suspicious activity and potential intrusions on their networks. By analyzing raw network traffic data, it identifies common attack patterns and flags unusual behavior. The output helps security teams understand what's happening and respond quickly to threats.
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