Using honeypots in a decentralized framework to defend against adversarial machine-learning attacks
Bibliographic record
Abstract
The market demand for online machine-learning services is increasing, and so to have are the threats to them. Adversarial inputs represent a new threat to Machine-Learningas- a-Services (MLaaSs). Meticulously crafted malicious inputs can be used to mislead and confuse the learning model, even in cases where the adversary only has access to input and output labels. As a result, there has been increased interest in defence techniques to combat these types of attacks. In this thesis, we propose a network of high-interaction honeypots as a decentralized defence framework that prevents an adversary from corrupting the learning model, primarily through the use of deception. We accomplish our aim by 1) preventing the attacker from correctly learning the labels and approximating the architecture of the black-box system; 2) luring the attacker away, towards a decoy model, using HoneyTokens; and 3) creating infeasible computational work for the adversary.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".