The integration of advanced technologies into telecommunication networks complicates troubleshooting, posing challenges for manual error identification in Packet Capture (PCAP) data. This manual approach, requiring substantial resources, becomes impractical at larger scales. Machine learning (ML) methods offer alternatives, but the scarcity of labeled data limits accuracy. In this study, we propose a self-supervised, large language model-based (LLMcap) method for PCAP failure detection. LLMcap leverages language-learning abilities and employs masked language modeling to learn grammar, context, and structure. Tested rigorously on various PCAPs, it demonstrates high accuracy despite the absence of labeled data during training, presenting a promising solution for efficient network analysis. Index Terms: Network troubleshooting, Packet Capture Analysis, Self-Supervised Learning, Large Language Model, Network Quality of Service, Network Performance.

翻译：随着先进技术融入电信网络，故障排查变得日益复杂，对数据包捕获（PCAP）数据的人工错误识别提出了严峻挑战。这种人工方法需要大量资源，在更大规模下变得不切实际。机器学习方法提供了替代方案，但标注数据的稀缺性限制了其准确性。本研究提出了一种基于大语言模型的自监督方法（LLMcap），用于PCAP故障检测。LLMcap利用语言学习能力，采用掩码语言建模来学习语法、上下文和结构。通过在多种PCAP数据上的严格测试，该方法在训练阶段无需标注数据的情况下仍表现出高精度，为高效网络分析提供了一个前景广阔的解决方案。索引术语：网络故障排查，数据包捕获分析，自监督学习，大语言模型，网络服务质量，网络性能。