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
About Network-Intrusion-Detection
vinayakumarr/Network-Intrusion-Detection
Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15
Implements deep learning architectures including CNNs and RNNs for binary and multi-class intrusion classification across three benchmark datasets. The approach evaluates shallow versus deep neural networks on preprocessed network traffic features, comparing detection effectiveness across different model topologies and depths. Provides reproducible research implementations with documented performance metrics for standardized security datasets commonly used in IDS evaluation.
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