Controlling spam and spear phishing via peered network overlays and non-repudiable traceback
Bibliographic record
Abstract
Despite 30 years of on-going effort, spam remains a significant problem. While technology has abated the deluge of spam invading the average user's email inbox, spam still facilitates the sale of counterfeited products, distribution of malware, and other criminal activities - as well as the more insidious use of spear phishing to leverage attacks into corporate and government networks. The value of email arises directly from its anyone-to-anyone message-passing capability. Hence, anti-spam techniques based on end-point encryption have met with limited success. Furthermore, due to geopolitical concerns, most traceback techniques only work effectively within - and not across - geopolitical boundaries; and while targeted removal of spam-friendly ISPs and botnets has had significant impacts on spam rates, these gains have tended to be short lived. This work proposes a novel approach to control spam and spear phishing through combining peer-level quality-of-service (QoS) agreements with a ProVerif verified, non-repudiable traceback protocol to enact spam resistant overlays that are: i) scalable, ii) enforceable over geopolitical boundaries, and iii) do not require technological sea changes. Simulation results on an Internet-style network of 3,000 ISPs show that even in the presence of aggressive spammers, it is possible to reduce the spam versus normal email equilibrium from 90:10 to 20:80. Furthermore, this approach can be used to aid in controlling spear phishing attacks targeting federated organizations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".