An Investigation on Packet Sampling between Kernel and User Space for NIDS
Published in International Symposium on Networks, Computers and Communications (ISNCC), Paris, France, 2025
We assess the feasibility of a partially in-kernel anomaly-based intrusion detection system that uses machine learning and a packet sampling policy to keep pace with network traffic. Using the same dataset as the inference stage, we identify a sampling threshold that maintains high precision and recall. Throughput tests on a two-middlebox testbed stressed with iperf3 show that the proposed system performs efficiently and is suitable for deployment.
Recommended citation: L. Giacometti, D. Crippa, S. Miano and G. Verticale, "An Investigation on Packet Sampling between Kernel and User Space for NIDS," 2025 International Symposium on Networks, Computers and Communications (ISNCC), Paris, France, 2025, pp. 1-6, doi: 10.1109/ISNCC66965.2025.11250446.
Download Paper | Download Bibtex
