A Critical Study on the Impact of Missing Data Imputation for Classifying Intrusions in Cyber-Physical Water Systems
Bibliographic record
Abstract
The performance of intrusion classification systems is often hampered by the presence of missing values in data collected from cyber-physical systems. Therefore, it is of paramount importance to robustly handle such missing scores, which in turn enhances the efficiency of intrusion classification task, and, consequently, the cybersecurity of cyber-physical systems. To this aim, this paper studies the efficacy of missing data imputation techniques for safeguarding intrusion classification systems against missing scores. To do this, a hybrid intrusion classification system is designed that comprises several advanced imputation techniques. To evaluate this intrusion classification framework, various incomplete scenarios have been simulated from data collected from a cyber-physical water system. In total, forty-four incomplete scenarios are considered throughout the experiments. The evaluation is conducted based on the classification accuracy and F-measure, as well as the root mean square error of the imputed data. The experimental results indicate the efficiency of the proposed intrusion classification system and find the best match missing data imputation technique for the sake of intrusion classification.
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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 | 0.001 | 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".