An Integrated Trustworthy Detection and Classification of Cyber-Physical Attacks in the Presence of Disturbances Using Morphological Image Processing and Explainable AI
Bibliographic record
Abstract
Smart Digital Substations (SDSs), are cyber-physical systems (CPSs) that rely on communication networks to exchange information among physical devices, making such CPSs vulnerable to cybersecurity threats. The problem of detecting and classifying attacks in SDSs has been traditionally studied by considering machine learning as a black box with no interpretation of the decisions made, which has led to untrustworthy algorithms. The attack detection becomes more challenging in the presence of disturbances, as certain types of attacks may exhibit similar behavior to some disturbance events. Furthermore, some attacks may concurrently occur in the presence of disturbances, which may increase the misclassification rate. This paper presents a novel trustworthy approach for detecting and classifying attacks considering the simultaneous occurrence of disturbances in SDSs. This study uses Explainable Artificial Intelligence (XAI) to provide interpretability of the classification decisions using the cyber and physical features in SDSs. This method applies a series of processes, including the use of the Bartlett observation window and morphological image processing, to enhance the learning of the Convolutional Neural Network (CNN) to better extract the hidden features relevant to the attacks and the disturbances when applying the Continuous Wavelet Transform. The proposed approach achieved detection and classification accuracies of 99.37% and 98.44%, while reducing the computational time by 90%, due to the incorporation of a hardware acceleration of multiple graphics processing units (GPUs).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".