Detecting heavy hitters, which are flows exceeding a specified threshold, is crucial for network measurement, but it faces challenges due to increasing throughput and memory constraints. Existing sketch-based solutions, particularly those using Comparative Counter Voting, have limitations in efficiently identifying heavy hitters. This paper introduces the Two-Factor Armor (2FA) Sketch, a novel data structure designed to enhance heavy hitter detection in data streams. 2FA Sketch implements dual-layer protection through an improved $\mathtt{Arbitration}$ strategy for in-bucket competition and a cross-bucket conflict $\mathtt{Avoidance}$ hashing scheme. By theoretically deriving an optimal $\lambda$ parameter and redesigning $vote^+_{new}$ as a conflict indicator, it optimizes the Comparative Counter Voting strategy. Experimental results show that 2FA Sketch outperforms the standard Elastic Sketch, reducing error rates by 2.5 to 19.7 times and increasing processing speed by 1.03 times.

翻译：检测超过特定阈值的大流对于网络测量至关重要，但由于吞吐量增加和内存限制，该任务面临挑战。现有的基于草图的解决方案，特别是那些使用比较计数器投票的方案，在高效识别大流方面存在局限。本文介绍了双层防护草图，这是一种新颖的数据结构，旨在增强数据流中的大流检测能力。2FA Sketch通过改进的桶内竞争$\mathtt{Arbitration}$策略和跨桶冲突$\mathtt{Avoidance}$哈希方案，实现了双层保护。通过理论推导最优参数$\lambda$并将$vote^+_{new}$重新设计为冲突指示器，它优化了比较计数器投票策略。实验结果表明，2FA Sketch性能优于标准弹性草图，将错误率降低了2.5至19.7倍，并将处理速度提高了1.03倍。