Online Neighborhood Watch: The Impact of Social Network Advice on Software Security Decisions
Bibliographic record
Abstract
Malicious software (malware) is one significant threat to Internet security. Malware is designed to harm a computer or network, and can be installed on one's machine without their consent. Attacks are often done by deceiving people into downloading malicious software that is posing as useful software. We speculated that if people had advice from a trusted source, they would be inclined to use the advice, reducing their chances of putting their computers at security risk. We designed and developed a system, Online Neighborhood Watch (ONWatch), to provide social network advice to users considering downloading software, sometimes offering alternatives when software was not trustworthy. We ran an empirical study to compare the advice coming from a trusted person to the advice coming from other more general social networks. We compared five different sources of advice in total. We did not find much evidence that the advice had a different effect based on the advisor, but the study confirmed our hypothesis that presenting alternative software will improve security.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".