Bibliographic record
Abstract
Multicasting has not been widely adopted until now, due to lack of access control over the group members. The authentication, authorization and accounting (AAA) protocols are being used successfully, in unicast communication scenarios, to control access to network resources. AAA protocols can be used for multicast applications in a similar way. However, without an effective sender access control, an adversary may exploit the existing IP multicast model, where a sender can send multicast data without prior authentication and authorization. Even a group key management protocol that efficiently distributes the encryption and the authentication keys to the receivers will not be able to prevent an adversary from spoofing the sender address and hence, flooding the data distribution tree. This can create an efficient Denial of Service attack. In previous work, we have proposed a framework for the use of AAA protocols to manage IP Multicast group membership. To prevent DoS attacks and other known attacks (e.g., replay attack), we propose in this paper an extension for sender access control. Our solution will authenticate and authorize each sender, and account for sender behavior by deploying AAA protocols. Moreover, a multicast packet will be forwarded to the distribution tree only if it is cryptographically authenticated at the entry point by the Access Router. The proposal we have presented provides a flexible authentication framework, supporting different authentication mechanisms, and is independent of the underlying routing protocol. Finally, we have extended our model to support inter-domain multicast groups.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".