A distributed content independent method for spam detection
Bibliographic record
Abstract
The amount of spam has skyrocketed in the recent past. Traditionally, spam was sent by single source mass mailers (spammers), making it relatively easy to screen out through the use of blacklists. Recently spammers started using botnets to send out the spam, rendering the blacklists ineffective. Although, content-based spam filters provide temporary relief, this is a never-ending cat-and-mouse game between spammers and filter developers. We propose a distributed, content independent, spam classification system that is specifically aimed at botnet generated spam and can be used in combination with existing spam classifiers. Our proposed system uses source identification in combination with a peer-to-peer based distributed database to identify e-mails that are likely to have originated from botnets. The system is distributed in order to provide a robust defense against denial-of-service attacks from the very same botnets. Lastly, our system is specifically designed to be used within the existing e-mail infrastructure. It does not require special hardware, changes to the underlying protocols, or changes to the mail transfer agents.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".