Secure Data Acquisition for Physics-based Side Channel Cybersecurity
Bibliographic record
Abstract
Cyber-physical systems (CPSs), such as planes and water treatment plants, are increasingly connected to the Internet. Connecting CPSs to the Internet allows for greater productivity but also increases the attack surface of these devices. Therefore, ensuring the security of CPSs is critical. One way to detect intrusions is through monitoring side-channel information, such as power consumption data. \n \n This thesis proposes a data acquisition unit (DAQ) to monitor and securely transmit power consumption data to the cloud for security applications. Anomaly detectors can then use this data to raise security-related alarms. As the DAQ is connected to the Internet, it is essential to consider security threats against it. To this end, this thesis conducts a security analysis using the Canadian government’s Harmonized Threat & Risk Assessment. In contrast to prior approaches, which have the detectors run locally, the detectors using the DAQ's data run in the cloud. This is enabled by the DAQ streaming the power consumption data to the cloud. Since the security-related anomaly detectors run in the cloud, they have access to more computational resources. Streaming has the additional advantage of scaling across multiple monitored devices. The DAQ is also significantly less expensive and more compact than an oscilloscope, which several prior methods use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".