Spam, Phishing, and the Looming Challenge of Big Botnets
Bibliographic record
Abstract
What could a spammer or phisher do with a botnet of a thousand machines? a hundred thousand? a million? Send lots of email is the least worrisome answer to these questions. As anti-spam and anti-phishing defenses improve, there is more than sufficient financial motivation for spammers and phishers to consider what they can accomplish with enormous scale. We begin by looking at a wide range of anti-spam defenses. Many of these, like rate limiting and port 25 blocking, will simply no longer work against big botnets; we explain why. Further, the basic cryptographic assumptions underlying the implementation of SSL certificates and DomainKeys/DKIM need re-examination in light of the massive computing power of big botnets. We describe possible attacks by spammers and phishers, and the implications these attacks have in terms of defense.
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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 | 0.001 | 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".