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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".