Distributed Layer-3 E-Mail Classification for Spam Control
Bibliographic record
Abstract
This paper proposes a distributed layer-3 e-mail classification for spam control. E-mail packets are inferred in transit and tagged with an intra-packet spam score to indicate whether the packet forms a legitimate or spam e-mail. During e-mail packet reassembly, tags for an e-mail are aggregated to give an inter-packet spam score. The naive Bayes inference technique is used to evaluate the performance of the proposed approach compared to the full e-mail classification approach. Our simulation results show that the proposed approach exhibits a comparable spam precision (and confidence) to the full e-mail classification approach. Spam recall increases from 63% to 85% depending to the maximum transmission unit size, approaching the 87% of the full e-mail classification. For 67% spam-to-legitimate ratio, we obtain reduction of end servers's workload by 42% to 57% (across all maximum transmission unit sizes tested) of the total e-mail traffic. Thus, the proposed approach can complement existing anti-spam systems by pre-processing e-mail packets on upstream nodes. Layer-3 e-mail processing requires reduced processing complexity as compared to layer-7 processing and is viable for high throughput hardware-based implementations
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".