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.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".