A Study of Trojan Propagation in Online Social Networks
Bibliographic record
Abstract
Online Social Networks (OSNs) are generally based on real social relations. Hence, malware writers are taking advantage of this fact to propagate their viral code into OSNs. In recent years, major OSNs, such as Facebook, were extensively under malware attacks. These attacks commonly lead to hundreds of thousands of compromised accounts that may bear personal and even confidential information. In this paper, different types of malware in OSNs are discussed. Then, this paper investigates the attacking vector of the Trojan type malware in OSNs. First, the clustering coefficient which is one of the main OSN graph characteristics is examined through simulation. It is shown that the clustering coefficient has a linear effect on the speed of Trojans. Second, the effect of user behavior is studied using different user reactions to malicious posts. Through simulations, we show that, if Trojans try to deceive users by choosing interesting topics, the speed of propagation will be increased exponentially. This effect raises the significance of giving security knowledge to avoid designated social engineered posts. Finally, we suggest adjustment to the current model for malware propagation in scale-free networks to consider the effect of clustering coefficient and the user behaviors.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".