MAD: One-Shot Machine Activity Detector for Physics-Based Cyber Security
Bibliographic record
Abstract
Side channel analysis offers several advantages over traditional machine monitoring methods. The low intrusiveness, independence with the host, data reliability and difficulty to bypass are compelling arguments for using involuntary emissions as input for enforcing security policies. However, side-channel information often comes in the form of unlabeled time series of a proxy variable of the activity. Enabling the definition and enforcement of high-level security policies requires extracting the state or activity of the system from the input data. We present in this paper a novel time series, one-shot pattern locator and classifier called Machine Activity Detector (MAD) specifically designed and evaluated for side-channel analysis. We evaluate MAD in two case studies on a variety of machines and datasets where it outperforms other traditional state detection solutions and presents formidable performances for security rules enforcement. Results of state detection with MAD enable the definition and verification of high-level security rules to detect various attacks without any interaction with the monitored machine.
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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 | 0.001 |
| 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".