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.
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 | 0.000 | 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 | 0.001 |
| 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".