Modelling Malicious Attack in Social Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Online Social Networks (OSNs) are based on actual trust relationships in environments which help people communicate with friends, family and acquaintances. Malicious individuals take advantage of this trust relationship to propagate malware through social networks. We study the dynamics of malware propagation among OSN users. Social networks users are referred to as nodes which is in two compartments: Healthy (H), or Infected (I). A H node could either be susceptible to infection (S) or removed (R). Simulations were carried out in R using the EpiModel network simulation package. Two networks were simulated thrice with different parameters to give better average values. Two categories of nodes, first category comprises of 3000 nodes with fewer connections and the second category comprising of 7000 nodes are the influential nodes with more connections. The larger network tends to have a higher fraction of nodes getting infected per unit time due to the high level of connectivity, as opposed to the small network where the number of connections is few. However, the infection tends to persist in the network as long as the birth rate is not equal to zero.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it