Robust adversarial learning and invariant measures
Bibliographic record
Abstract
A number of open cyber-security challenges are arising due to the rapidly evolving scale, complexity, and heterogeneity of modern IT systems and networks. The ease with which copious volumes of operational data can be collected from such systems has produced a strong interest in the use of machine learning (ML) for cyber-security, provided that ML can itself be made sufficiently immune to attack. Adversarial learning (AL) is the domain focusing on such issues and an arising AL theme is the need to ensure that ML solutions make use of robust input measurement features (i.e., the data sets used for ML training must themselves be robust against adversarial influences). This observation leads to further open questions, including: “What formally denotes sufficient robustness?”, “Must robust features necessarily exist for all IT systems?”, “Do robust features necessarily provide complete coverage of the attack space?”, etc. This work shows that these (and other) open AL questions can be usefully re-cast in terms of the classical dynamical system's problem of needing to focus analyses on a system's invariant measures. This re-casting is useful as a large body of mature dynamical systems theory exists concerning invariant measures which can then be applied to cyber-security. To our knowledge this the first work to identify and highlight this potentially useful cross-domain linkage.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".