Bibliographic record
Abstract
In this paper, we present the detailed design and analysis of our solution to the IP traceback problem. We adopt (at the AS level) a path signature generation method which was proposed at the router level to primarily provide a means of filtering attack traffic. Our solution assumes a secure routing infrastructure to exchange authenticated messages in order to learn path signatures. We envision the local adoption of a separate, yet complementary, traditional traceback system at each AS. This solution is hierarchical in the sense that it works at the autonomous system (AS) level first then once a small list of possible source ASes is identified, those ASes are queried and traceback is performed within each AS to prune the list down to the actual source. Using simulation results we demonstrate that our solution is practical since it reduces - as a first step - the search space from the entire router space of the Internet to an AS-list that is only a very small fraction of all possible ASes. This combination is more scalable than doing a flat IP traceback on the entire router space of the Internet. We go on to propose a means of using more than 16 bits of the IP fragmentation fields which are traditionally used by various IP traceback systems. We present results based on using various sizes for the marking field, as well as varying number of total marks and different sizes for each mark.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".