Do malware warnings reduce the likelihood of installing bad software? The case of the Trojan Horse
Bibliographic record
Abstract
My research focused on increasing computer security by reducing users' likelihood of installing Trojan Horses: malware hiding inside attractive software.Social cognition research suggests that reading security warnings in software reviews could reduce the likelihood of installing malware.In Study 1, 43 undergraduates viewed 30 user reviews of hypothetical games.Half the reviews were malware warnings.Ratings of the warnings' strength were used to select strong and weak warnings for Study 2. In Study 2, 45 undergraduates viewed descriptions and reviews of real computer games.I manipulated the strength and number of warnings in the reviews.Results indicated that the likelihood of installing a game was influenced by both the number and strength of malware warnings in reviews: two warnings reduced ratings of installation likelihood more than did one warning; strong warnings reduced the ratings more than did weak ones.Implications and limitations of the findings for social contributions to computer security are discussed.iii Acknowledgements "Real life isn't always going to be perfect or go our way, but the recurring acknowledgement of what is working in our lives can help us not only to survive but surmount our difficulties" -anonymous Apart from my yearlong effort, this thesis materialized because of the continuous guidance, support, and encouragement of many people.The small space of this page is not enough to elaborate on and to do justice to their contributions, but I want to express my gratitude to each one of them.First, I would like to thank my co-supervisors, Dr. Robert Biddle and Dr. Warren Thorngate, who met with me weekly since September, 2011 to discuss my thesis.Their enthusiastic comments and feedback developed my thesis towards completion.Next, I want to thank the members of
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 | 0.005 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".