Anomaly detection in deterministic state-transition systems with deep sequential models and confidence decomposition
Artykuł w czasopiśmie
MNiSW
100
Lista 2024
| Status: | |
| Autorzy: | Tokarski Piotr, Łazaruk Karol, Plechawska-Wójcik Małgorzata, Miłosz Marek, Nguyen Huu Quy, Dzieńkowski Mariusz, Gałka Łukasz |
| Dyscypliny: | |
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| Rok wydania: | 2026 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Wolumen/Tom: | 11 |
| Strony: | 1 - 13 |
| Impact Factor: | 4,2 |
| Web of Science® Times Cited: | 0 |
| Bazy: | Web of Science | IEEE Xplorer |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 6 listopada 2026 |
| Abstrakty: | angielski |
| In this article, the problem of detecting undesirable transitions in deterministic state transition systems in the absence of complete and reliable anomaly labels is considered. In contrast to classical methodologies of anomaly detection in time series, which model the distribution of trajectories as a stochastic process, the analyzed system is deterministic - each state is uniquely determined by the previous state and the operation performed. The proposed framework, designated as USTAD (Uncertainty-aware State Transition Anomaly Detection), integrates deterministic state-trajectory reconstruction, deep sequential modeling, and a threshold-based decomposition of the model score into confidence components. This approach facilitates the identification of redundancies and structural returns in the trajectory, without manual annotation or domain-specific rules. The reconstructed trajectories are used only to generate training labels. The trained model works on a window of past states and moves, so it can indicate a redundant path before the return to an earlier state occurs. The score is decomposed into the certainty of the normal class, the certainty of the anomalous class and a residual uncertainty, which indicates borderline decisions. This is of particular importance in conditions of strong class imbalance and limited supervision. The method has been experimentally verified in the context of speedcubing, a complex yet deterministic system characterised by state transitions. The findings suggest that the proposed solution exhibits superior performance on metrics sensitive to data imbalance, and that the confidence decomposition supports selective decisions on borderline windows. |
