Enhancing Network Intrusion Detection: An AutoML Pipeline with Efficient Digital Twin Synchronization
Bibliographic record
Abstract
In the era of 5G and Beyond (5G+) networks, characterized by increased complexity and vulnerability to cyberthreats, the detection of cyberattacks within network traffic becomes more challenging. Machine Learning (ML) offers a promising solution for detecting cyberthreats. However, the constantly ever-evolving technology landscape introduces rapidly evolving attacks, requiring continuous ML model updates. Accordingly, this paper leverages the power of Automated ML (AutoML) and Digital Twin (DT) technologies to deploy an Intrusion Detection System (IDS) in resource-constrained environments, which remains effective over time. An AutoML pipeline is proposed in this paper for multi-class network attack detection, consisting of three offline and automated phases-data preprocessing, feature engineering, and model learning-and an online phase for model monitoring and updates. Additionally, a DT has been introduced to continually update and evolve the ML model in response to the dynamic nature of new attacks, emphasizing low overhead and efficient synchronization. Specifically, two data generation approaches within the DT are explored: uniform sampling based on statistical properties and generative models (such as Variational AutoEncoders (VAEs) and Generative Adversarial Networks (GANs)) using raw data. The experimental results demonstrate that uniform sampling achieves the fastest recovery, lowest overhead, and highest privacy in enhancing the multi-layer perceptron, the best-performing ML model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".