A Neural Word Embedding Approach to System Trace Reconstruction
Bibliographic record
Abstract
Data generated by real-world systems specifically cyber-physical systems is full of noise, packet loss, and other imperfections. However, most intrusion detection, anomaly de-tection, monitoring, and mining algorithms and frameworks assume that data provided is of perfect quality. Therefore, these algorithms tend to perform extremely well in controlled lab environments but fail in the real-world. We propose a method for accurately restoring discrete tem-poral or sequential system traces affected by data loss, using Word 2vec's Continuous Bag of Words (CBOW) model. The model works by learning to predict the next event in a sequence of events, the model feeds its output back into it for subsequent future predictions. Such a method can reconstruct even long sequence of missing events, and help validate and improve data quality for noisy data. The restored traces are very close to the real-data and can be used by algorithms depending on real-data for system analysis. We demonstrate our method by reconstructing traces from QNX real-time operating system consisting of long sequences of discrete events. We show that given even small parts of a QNX trace, our CBOW model can predict future events with an accuracy of almost 90% outperforming the Markov Model benchmark.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".