Graph Neural Network Framework for Advanced Persistent Threat Detection in IIoT Environments
Bibliographic record
Abstract
The increasing reliance of industrial infrastructures on the Industrial Internet of Things (IIoT) has made them more vulnerable to complex cyberattacks, especially Advanced Persistent Threats (APTs). To recognize and analyze these multi-stage incursions, we require computational models that can capture both structural and temporal connections in IIoT network architectures. This paper presents a framework based on Graph Neural Networks (GNNs) for detecting and classifying APTs in IIoT settings. We use the CICAPT-IIoT2024 dataset, which provides realistic multi-phase APT attack scenarios. The approach models system components, network communications, and process interactions as nodes and edges in a dynamic graph, allowing for relational learning and context-aware feature extraction. The GNN architecture leverages graph connectivity patterns and message-passing techniques to identify attack phases with greater accuracy and robustness. Experimental results show that this method outperforms traditional deep learning techniques and ensemble methods, particularly in early-stage anomaly detection. This paper highlights the potential of graph-based learning as an effective way to enhance IIoT infrastructure security against the changing behaviors of advanced persistent threats.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".