A Study of XSS Worm Propagation and Detection Mechanisms in Online Social Networks
Bibliographic record
Abstract
We present analytical models and simulation results that characterize the impacts of the following factors on the propagation of cross-site scripting (XSS) worms in online social networks (OSNs): 1) user behaviors, namely, the probability of visiting a friend's profile versus a stranger's; 2) the highly clustered structure of communities; and 3) community sizes. Our analyses and simulation results show that the clustered structure of a community and users' tendency to visit their friends more often than strangers help slow down the propagation of XSS worms in OSNs. We then present a study of selective monitoring schemes that are more resource efficient than the exhaustive checking approach used by the Facebook detection system which monitors every possible read and write operation of every user in the network. The studied selective monitoring schemes take advantage of the characteristics of OSNs such as the highly clustered structure and short average distance to select only a subset of strategically placed users to monitor, thus minimizing resource usage while maximizing the monitoring coverage. We present simulation results to show the effectiveness of the studied selective monitoring schemes for XSS worm detection.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".