GWAD: Greedy Workflow Graph Anomaly Detection Framework for System Traces
Bibliographic record
Abstract
System traces are a collection of time-stamped messages recorded by the operating system while the system is running. Analysis of these traces is crucial for tasks such as system fault finding. Moreover, detecting anomalies in system behavior becomes crucial in safety-critical and time-sensitive systems where delayed detections can lead to catastrophic outcomes. Therefore, we focus on developing a lightweight and explainable approach for safety-critical time-sensitive systems.Given a set of system traces under normal conditions and anomalous conditions, trace-based anomaly detection aims at classifying the trace as anomalous or not. In this work, we introduce GWAD, a greedy workflow graph framework for anomaly detection, a novel greedy graph construction approach for both offline and online anomaly detection in system traces. Our approach utilizes both sequence of occurrence of events and the time interval between their occurrences in learning the normal system behavior. We propose two approaches, first for offline classification of the trace as anomalous or normal using the event occurrence workflow graphs and secondly an online streaming algorithm that monitors the events as they occur in real-time for detecting anomalies increasing system resilience. Our approach also provides reasoning for the cause of anomalous behavior. We show that GWAD is better than traditional state-of-the-art models. The paper shows the technical feasibility and viability of GWAD through multiple case studies using traces from a field-tested hexacopter.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".