A Data-Driven Wireless Intrusion Detection Framework Based on Digital Twin
Bibliographic record
Abstract
Wireless intrusion detection (WID) faces persistent challenges of high false alarm rates and scarcity of attack data for training. This paper proposes a digital twin-assisted WID framework that integrates real-time network simulation with live data feeds to enhance detection accuracy. A network digital twin, implemented in network simulator version 3 (ns-3), runs in parallel with the physical wireless network and mirrors its behavior via online synchronization by using Message Queuing Telemetry Transport (MQTT), and generates simulated traffic for anomaly analysis. We leverage real wireless intrusion datasets, including the Aegean Wi-Fi Intrusion Dataset (AWID2), the Canadian Institute for Cybersecurity - Internet of Things (CICIoT) 2023 dataset, and a 5G Core Packet Forwarding Control Protocol (PFCP) dataset, to train and evaluate our intrusion detection models, and the system architecture, data pipeline, simulation-physical coupling mechanisms, anomaly scoring algorithm, and automated attack script generation are described in detail. Experimental results demonstrate that our digital twinassisted IDS achieves higher F1-scores and true positive rates with lower false positive rates than baseline IDS approaches on the real datasets.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".