A Two-Stage Confidence-Based Intrusion Detection System in Programmable Data-Planes
Bibliographic record
Abstract
The frequent occurrence of network attacks highlights the criticality of developing effective intrusion detection systems (IDSs) that can promptly detect and respond to malicious flows. The proliferation of programmable devices has opened up new possibilities for integrating intelligent IDSs into the data-plane. This allows the execution of machine learning (ML)-based detection models at line-rate, meeting the low latency requirements of anomaly detection. We propose a two-stage confidence-based Intrusion Detection System (TSCIDS) that aims at early detection while considering the level of certainty of prediction. The control-plane adopts a customized transfer learning scheme, wherein two interdependent convolutional neural network (CNN) models are trained, one using the early context of flows and the other adding the later context. A post-hoc calibration method is applied to improve the performance of models. TSCIDS detects anomalous behavior in different phases of flows while allowing the latter CNN to leverage the hidden state of the early CNN. TSCIDS ensures that the two CNN models are integrated into the data-plane pipeline by building the inference steps of CNN into different modules, using switch-supported operations. Simulation results show that the calibrated model can detect more attacks in the early phase compared to the uncalibrated model. Additionally, the training scheme saves the memory consumption of running models on programmable devices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".