QUICPro: Integrating Deep Reinforcement Learning to Defend against QUIC Handshake Flooding Attacks
Bibliographic record
Abstract
In recent years, QUIC protocol has emerged as a promising alternative to traditional transport protocols like TCP and UDP, offering significant performance improvements in latency and throughput. Additionally, QUIC provides strong security protection mechanisms. However, like most other new protocols, QUIC also faces new security challenges. In our previous study, we found that handshake flooding attacks can exploit vulnerabilities in the QUIC handshake process to overwhelm server resources and disrupt service availability. In this lightening paper, we present QUICPro, a novel approach that leverages Deep Reinforcement Learning (DRL) techniques for dynamic network security optimization to enhance QUIC protocol security against handshake flooding attacks. By integrating DRL algorithms with adaptive defence mechanisms, QUICPro offers a proactive and adaptive solution capable of detecting and mitigating handshake flooding attacks in real time. This paper analyzes the QUICPro framework, highlighting its technical components, implementation details, and expected outcomes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".