QUICShield: A Rapid Detection Mechanism Against QUIC-Flooding Attacks
Bibliographic record
Abstract
QUICis a modern transport layer internet protocol designed to be more efficient and secure than TCP (Transmission control protocol). However, QUIC remains vulnerable to handshake flooding attacks due to its similar design to TCP in the handshaking process. This paper introduces an innovative defence mechanism, QUICShield, which enables rapid detection and protection from QUIC-flooding DDoS attacks across different IP spoofing scenarios. QUICShield is a Bloom filter-based technique that provides rapid change detection to distinguish between incomplete or invalid handshakes and legitimate connections while accounting for common handshake errors. It utilizes the probabilistic data structure of Bloom Filter to detect malicious traffic effectively and incorporates change detection techniques to adapt to evolving attack patterns. Also, it addresses the unique challenges of QUIC-Flooding attacks, which exploit the protocol's stateless nature and the inclusion of cryptographic computations to overwhelm a target's computational resources. Existing defence mechanisms against DDoS attacks primarily focus on TCP SYN-Flooding. Although these approaches are effective in the TCP domain, they are inadequate in addressing the specific vulnerabilities related to the QUIC protocol. Our QUICShield technique fills this gap by offering a customized solution for QUIC-based systems. It neutralizes malicious traffic, maintains legitimate connections, and adapts to IP spoofing in the QUIC protocol networks. Furthermore, QUICShield defends against QUIC-Flooding DDoS attacks, with real attack emulation demonstrating improved detection of previously ineffective invalid packets, boosting network resilience against security threats.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".