An Empirical Approach to Evaluate the Resilience of QUIC Protocol Against Handshake Flood Attacks
Bibliographic record
Abstract
QUIC is a new transport protocol aiming to enhance web connection performance and security. It was gaining popularity quickly in recent years and has been adopted by a number of prominent tech companies, including Facebook, Amazon, and Google. However, the resilience of QUIC Protocol against various cyber attacks has not been fully tested yet. In this paper, we investigate the resilience of QUIC Protocol against handshake flood attacks. We conducted comprehensive experiments to evaluate the resource consumptions of both the attacker and the target during incomplete handshake attacks, including CPU, memory, and bandwidth. The DDoS amplification factor was measured and analyzed based on the results. We compared the results against TCP Syn Cookies under Syn flood attacks. We show that the QUIC Protocol design has a much larger DDoS amplification factor compared to the TCP Syn Cookies, which means QUIC is more vulnerable to handshake DDoS attacks. Also, the CPU resource of QUIC servers is most likely the bottleneck during the handshake flood attacks. To the best of our knowledge, this is the first study to thoroughly investigate resilience of QUIC to handshake DDoS attacks.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".