Autonomous system based flow marking scheme for IP-Traceback
Bibliographic record
Abstract
Tracing IP packets to their sources, known as IP-Traceback, is a critical task in defending against IP spoofing and DoS attacks. There are several solutions to traceback to the origin of the attack. However, all these solutions require either all routers or ISPs to support the same IP-Traceback mechanism. To address this limitation, we propose an IP-Traceback approach at the level of autonomous systems, called Autonomous System-based Flow Marking, ASFM, to identify some key locations in the path where attacker packets are being forwarded. ASFM employs the BGP update message community attribute that enables information to be passed across ASs even if they are not necessarily involved in the IP-Traceback scheme. We also propose an authentication method, so a downstream AS can examine the correctness of the marking provided by the upstream ASs, thus eliminating the fake marking embedded by subverted routers. Finally, we evaluate and analyze the performance of our proposal, using real life datasets.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".