Denial of Service Attacks in Networks with Tiny Buffers
Bibliographic record
Abstract
Recently, several papers have studied the possibility of shrinking buffer sizes in Internet core routers to just a few dozen packets under certain constraints. If proven right, these results can open doors to building all-optical routers, since a major bottleneck in building such routers is the lack of large optical memories. However, reducing buffer sizes might pose new security risks: it is much easier to fill up tiny buffers, and thus organizing Denial of Service (DoS) attacks seems easier in a network with tiny buffers. To the best of our knowledge, such risks have not been studied before; all the focus has been on performance issues such as throughput, drop rate, and flow completion times. In this paper, we study DoS attacks in the context of networks with tiny buffers. We show that even though it is easier to fill up tiny buffers, synchronizing flows is more difficult. Therefore to reduce the network throughput, the attacker needs to utilize attacks with high packet injection rates. Since such attacks are easily detected, we conclude that DoS attacks are in fact more difficult in networks with tiny buffers.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".