The effectiveness of vaccinations on the spread of email-borne computer viruses
Bibliographic record
Abstract
In the last decade, computer viruses have caused tremendous losses to organizations. New viruses continue to cause havoc, in spite of having better antivirus software. It is thus imperative that we understand what factors significantly influence the spread of viruses. In this paper, we model the networks of users as graphs. For simplicity, we assume that every user works only on their own computer. We assume that nodes in the graph are initially susceptible to infection. Once infected, a node may spread the virus (using perhaps, the inbox or address book) until either the virus is removed and the node is immunized. We assume that an immunized node never gets the same virus again. We study the effect of vaccinations in containing the spread of email-borne computer viruses on some traditional structured interconnection graphs, as well as "Internet-like" small-world graphs. We test the effectiveness of vaccinations in containing the spread of viruses by looking at the total number of nodes infected for different values of the delay D which is incurred in detecting a virus, developing a vaccine and immunizing each infected node. Our simple user model assumes that a user is likely to open an email attachment (and activate the virus) with probability p and delete it with probability 1 - p. Intuitively, one would expect that the fraction of nodes infected would increase slowly as p is varied or as D is varied. Using simulations, we demonstrate that while this is true for the traditional graphs we used, it does not hold for small-world graphs. Since user networks are known to be small-world graphs, this implies that vaccinations are far less effective on real networks than they would be if the user networks were like traditional interconnection graphs
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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".