Genos, a general in-network framework for unsupervised anomaly-based network intrusion detection, achieves high throughput, interpretability, and trivial updating overhead by extracting model-agnostic rules.
To address the class imbalance problem in the Bot-IoT dataset, a binary classification method with synthetic minority over-sampling techniques (SMOTE) is proposed to effectively detect attack packets in IoT network traffic.