论文标题
通过基础模型重新思考数据驱动的网络：挑战和机遇
Rethinking Data-driven Networking with Foundation Models: Challenges and Opportunities
论文作者
论文摘要
基础模型导致了人工智能（AI）系统的范围转变。他们对自然语言处理（NLP）和其他几个领域产生了重大影响，不仅减少了所需的标记数据数量，甚至消除了对其的需求，而且还可以显着提高各种任务的性能。我们认为基础模型可能会对网络流量分析和管理产生类似的深远影响。更具体地说，我们表明网络数据共享了基础模型在语言学中成功的几个属性。例如，网络数据包含丰富的语义内容，以及几个网络任务（例如，流量分类，从规范文本中生成协议实现，异常检测）可以在NLP中找到类似的对应物（例如，情感分析，从自然语言到代码的翻译到代码，分发外部分发）。但是，网络设置还提出了必须克服的独特特征和挑战。我们的贡献在于强调基础模型和网络交集的机会和挑战。
Foundational models have caused a paradigm shift in the way artificial intelligence (AI) systems are built. They have had a major impact in natural language processing (NLP), and several other domains, not only reducing the amount of required labeled data or even eliminating the need for it, but also significantly improving performance on a wide range of tasks. We argue foundation models can have a similar profound impact on network traffic analysis, and management. More specifically, we show that network data shares several of the properties that are behind the success of foundational models in linguistics. For example, network data contains rich semantic content, and several of the networking tasks (e.g., traffic classification, generation of protocol implementations from specification text, anomaly detection) can find similar counterparts in NLP (e.g., sentiment analysis, translation from natural language to code, out-of-distribution). However, network settings also present unique characteristics and challenges that must be overcome. Our contribution is in highlighting the opportunities and challenges at the intersection of foundation models and networking.
