TurboRetry: Mitigating Large-Scale QUIC Handshake Floods with Off-the-Shelf DPU Offloading
Jiahao Wu, Heng Pan, Kai Lv, Zhenyu Li, Yanbiao Li, Gaogang Xie
Abstract
The modern transport protocol QUIC is designed to enhance network performance and security, but it remains vulnerable to handshake flooding attacks. Such attacks exhaust CPU resources by forcing the server to perform expensive cryptographic operations via a large number of handshaking requests. QUIC provides a built-in defense mechanism, the Retry mechanism, to mitigate these attacks. However, our experiments reveal that it can still become a performance bottleneck under large-scale QUIC handshake floods due to substantial computational overhead. In this paper, we design and implement TurboRetry, a split design, that offloads the Retry mechanism onto DPUs to efficiently mitigate QUIC handshake floods. TurboRetry partitions the tasks of the Retry into two categories, and then assigns them to the DPUs and the host, respectively. To preserve QUIC semantics and reduce the coordination overhead, TurboRetry designs an extended Retry token format and an efficient cooperation scheme. In addition, TurboRetry offloads the connection authorization task to the on-path DPA to further improve both performance and security. Our evaluation shows that TurboRetry outperforms the host-side implementation by a wide margin, improving throughput by 10-20×.
Create a lesson
Related papers
Analog Pin Directionality as an Exfiltration Attack Surface in Mixed-Signal ICs
Ramana Ranganatham, Chirag Adiga, Michael Zuzak et al.
Characterizing Network Centralization and Observability in the Remote MCP Ecosystem
Muhammad Abdullah Sohail
When Agents Look Like Beacons: NIDS Evasion by Model Context Protocol Traffic
Muhammad Abdullah Sohail
Hamming Ideals and Grobner Bases for ISD-like Syndrome Decoding
Roberto La Scala, Marco Marchesin, Sharwan K. Tiwari
ASLEval: Measuring Privacy Exposure Displacement in LLM Agent Sessions
Guosen Wu, Huizhen Huang, Guoxiong Long et al.
CASHEWS: Source Preprocessor for LLM-based Malicious Package Detection
Jean-Charles Noirot Ferrand, David Adei, Anders Møller et al.