Passive Hybrid Network-Based Intrusion Detection System (Hybrid-NIDS) Combining Suricata and Random Forest

2026-09-21

AI Summary

本文评估了一种结合Suricata和随机森林的被动混合网络入侵检测系统，解决了特征泄漏问题，并通过实验表明强基准性能不一定转化为实际操作有效性。

Abstract

This paper evaluates a passive Hybrid Network-based Intrusion Detection System (Hybrid-NIDS) prototype that combines Suricata with Random Forest flow classification and centralized ELK-based alert handling. The study explicitly separates benchmark evaluation from PCAP/live operational validation and controls exact feature-duplicate leakage using feature hashing and group-aware splitting. From 2,540,047 UNSW-NB15 records, 453 conflicting-label hash groups containing 1,879 rows were removed; the resulting Development and Hold-out sets have zero exact feature-hash overlap. RF-41 achieved F1 = 0.971360 and ROC-AUC = 0.999671, while the NFStream-compatible RF-21 achieved F1 = 0.970148 on the same prepared hold-out boundary. However, operational validation revealed substantial benchmark-to-deployment domain shift: on a labeled laboratory PCAP, RF-21 and the strictly correlated branch achieved recall of only 0.0095, and RF-21 produced no alerts in five additional 60-second attack sessions. An unlabeled normal-traffic test produced 439 alerts from 2,375 flows; this value is reported only as an alert ratio and is not interpreted as a false-positive rate. These results show that strong performance on a public benchmark does not directly translate into operational effectiveness. Accordingly, the current Hybrid-NIDS should be interpreted as a passive prototype and evaluation framework, and the reported experiments do not demonstrate that Suricata-Random Forest correlation provides better operational detection than Suricata alone.

Problem

• Hybrid-NIDS
• domain shift
• operational validation

Innovation

• Hybrid-NIDS
• feature hashing
• group-aware splitting
• ELK-based alert handling