Watchmen: Scalable Cheat-Resistant Support for Distributed Multi-player Online Games
Bibliographic record
Abstract
Multi-player online games are inherently distributed applications, and a wide range of distributed architectures have been proposed. However, only few successful commercial systems follow such approaches, even given their benefits, due to one main hurdle: the easiness with which cheaters can disrupt the game state computation and dissemination, perform illegal actions, or unduly gain access to sensitive information. The challenge is that any measures used to address cheating must meet the heavy scalability and tight latency requirements of fast paced games. We propose Watchmen, the first distributed scalable protocol designed with cheat detection and prevention in mind that supports fast paced games. It is based on a randomized dynamic proxy scheme for both the dissemination and verification of actions. Furthermore, Watchmen reduces the information exposed to players close to the minimum required to render the game. We build our proof-of-concept prototype on top of Quake III. We show that Watchmen, while scaling to hundreds of players and meeting the tight latency requirements of first person shooter games, is able to significantly reduce opportunities to cheat, even in the presence of collusion.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".