论文标题
CC-Fuzz：基于遗传算法的压力测试控制算法的模糊算法
CC-Fuzz: Genetic algorithm-based fuzzing for stress testing congestion control algorithms
论文作者
论文摘要
在过去的几年中，拥堵控制研究的兴趣大大增加，许多专用算法都考虑到了特定应用的需求。这些算法在部署在Internet上之前经过有限的测试，它们与其他拥堵控制算法进行交互并在各种网络条件下进行运行。由于算法不足或实现错误，这通常会导致野外的性能问题，并且由于数据包痕迹没有可用，这些问题通常很难识别。 在本文中，我们提出了CC-Fuzz，这是一种自动拥堵控制测试框架，该框架使用遗传搜索算法，以通过产生对抗性网络痕迹和流量模式来强调交通拥堵控制算法。使用这种方法的初始结果很有希望-CC-Fuzz自动在BBR中发现了一个错误，该错误使其永久失速，并且能够自动发现众所周知的低率TCP攻击等。
Congestion control research has experienced a significant increase in interest in the past few years, with many purpose-built algorithms being designed with the needs of specific applications in mind. These algorithms undergo limited testing before being deployed on the Internet, where they interact with other congestion control algorithms and run across a variety of network conditions. This often results in unforeseen performance issues in the wild due to algorithmic inadequacies or implementation bugs, and these issues are often hard to identify since packet traces are not available. In this paper, we present CC-Fuzz, an automated congestion control testing framework that uses a genetic search algorithm in order to stress test congestion control algorithms by generating adversarial network traces and traffic patterns. Initial results using this approach are promising - CC-Fuzz automatically found a bug in BBR that causes it to stall permanently, and is able to automatically discover the well-known low-rate TCP attack, among other things.
