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Ieee Nids Report by nch84041 is a document available to read on EtoBox.

This paper conducts a comparative study of eight machine learning and deep learning models for network intrusion detection using the CIC-IDS dataset. It evaluates binary anomaly detection and multi-class attack classification, finding that LightGBM excels in binary detection while TabNet leads among deep learning models. The study provides insights into model performance, preprocessing effects, and inference throughput, contributing valuable diagnostics for practitioners in the field.

Author

nch84041

Language

EN