Submission #3: A Data-Plane-Only Approach to Accurate Persistent Flow Detection on Programmable Switches in High-Speed Networks ================================================================================================ Abstract -------- In high-speed data center networks, persistent flows are repeatedly observed over extended periods, potentially signaling threats such as stealthy DDoS or botnet attacks. Monitoring every flow in production-grade hardware switches that feature limited memory, however, is challenging under typical high flow rates and data volumes. To tackle this, approximate data structures—i.e., sketches—are often employed in cutting-edge programmable switches. Yet many existing methods rely on per-time-window flag resets, which require frequent control-plane interventions that make them unsuitable for high-speed traffic. This paper introduces Pallas, a fully data-plane-implementable sketch for detecting persistent flows in high-speed networks with high accuracy. Authors ------- 1. Weihe Li (University of Edinburgh) 2. Beyza Bütün (IMDEA Networks Institute and Universidad Carlos III de Madrid) 3. Tianyue Chu (IMDEA Networks Institute) 4. Marco Fiore (IMDEA Networks Institute) 5. Paul Patras (The University of Edinburgh)