CC-Fuzz: Genetic Algorithm-Based Fuzzing for Stress Testing Congestion Control Algorithms

@inproceedings{2022-Ray-hotnets,
    author = "Ray, Devdeep and Seshan, Srinivasan",
    title = "CC-Fuzz: Genetic Algorithm-Based Fuzzing for Stress Testing Congestion Control Algorithms",
    year = "2022",
    isbn = "9781450398992",
    publisher = "Association for Computing Machinery",
    address = "New York, NY, USA",
    url = "https://doi.org/10.1145/3563766.3564088",
    doi = "10.1145/3563766.3564088",
    abstract = "Recent congestion control research has focused on purpose-built algorithms designed for the special needs of specific applications. Often, limited testing before deploying a CCA results in unforeseen and hard-to-debug performance issues due to the complex ways a CCA interacts with other existing CCAs and diverse network environments. We present CC-Fuzz, an automated framework that uses genetic search algorithms to generate adversarial network traces and traffic patterns for stress-testing CCAs. Initial results include CC-Fuzz automatically finding a bug in BBR that causes it to stall permanently, and automatically discovering the well-known low-rate TCP attack, among other things.",
    booktitle = "Workshop on Hot Topics in Networking (HotNets)",
    pages = "31–37",
    numpages = "7",
    keywords = "fuzz testing, congestion control, genetic algorithm",
    location = "Austin, Texas",
    series = "HotNets '22",
    month = "November"
}