  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.
