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 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 | 0.000 |
| 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".