HSM-Based Architecture to Detect Insider Attacks on Server-Side Data
Bibliographic record
Abstract
In this paper, we propose an HSM-based architecture to detect insider attacks on server-side data. Our proposed architecture combines four cryptography-based defense mechanisms: Nonce-Based Process Authentication (NBPA), Hash-Based Field Integrity (HBFI), Hash-Based Field Availability (HBFA), and Hash-Based Row Availability (HBRA). This novel architecture is designed to detect a predefined comprehensive attack model on server-side data tailored for an HSM-based architecture. The implementation results show that the throughput decrease is mostly manageable (14% for NBPA, 30-50% for HBFI, 25% for HBFA, and 43.74% for the combination of all mechanisms), with the indication that some mechanisms are more or less appropriate depending on the situation. Moreover, the HBRA mechanism performed well regarding the attack detection time (5 minutes for a database of 1000 entries).
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 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".