QUICwand: A Machine Learning Optimization-Based Hybrid Defense Approach Against QUIC Flooding Attacks
Bibliographic record
Abstract
QUIC is an emerging transport layer network protocol which provides enhanced security and performance over traditional transport protocols such as TCP and UDP. QUIC aims to provide improved performance and security compared to TCP + TLS 1.3. It also serves as the foundation for HTTP/3. Nevertheless, QUIC is vulnerable to handshake flooding attacks because of its handshaking mechanism. In this paper, we propose “QUICwand,” a State-of-the-Art Network Security framework to defend against QUIC flooding attacks. QUICwand leverages Bayesian Optimization within a modified Bloom filter to defend against QUIC flooding attacks. Explicitly, QUICwand detects and mitigates QUIC flooding attacks to reduce false positive rates significantly, as demonstrated in empirical evaluations. A key design in QUICwand is the dynamic optimization of the Bloom Filter, which demonstrates significant improvement in detection rates and false positive rates over its predecessor QUICShield. The Machine Learning core of QUICwand automatically fine-tunes the defense mechanisms to ensure timely and effective detection and mitigation. Our experimental results demonstrate that QUICwand substantially advances network security and resiliency against QUIC flooding 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".