mlsanityData to 14 Oct 2026

LLMcap: Large Language Model for Unsupervised PCAP Failure Detection

arXiv 2407.06085 ↗First posted 3 Jul 2024cs.LG cs.AI cs.NIcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 7 of its 15 references that are in the map. First posted on arXiv 3 Jul 2024.

Builds on

7 of 15 references.

References · 7

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  4. 2021LogBERT: Log Anomaly Detection via BERTcited by 118
  5. 2021LAnoBERT: System Log Anomaly Detection based on BERT Masked Language Modelcited by 30
  6. 2021Datasets are not Enough: Challenges in Labeling Network Trafficcited by 13
  7. 2022When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classificationcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 5.

7 papers.

  1. 2025A Survey of AIOps in the Era of Large Language Modelscited by 19
  2. 2026PLUME: Building a Network-Native Foundation Model for Wireless Traces via Protocol-Aware Tokenizationcited by 2
  3. 20255G Core Fault Detection and Root Cause Analysis using Machine Learning and Generative AIcited by 1
  4. 2026Evidence-Grounded Ensemble Diagnosis of 802.11 Packet Captures: A Multi-Stage Pipeline with Deterministic Reliability Scoringcited by 0
  5. 2026Protocol-Aware Tokenization and Architecture Co-Design for Wireless Packet Foundation Modelscited by 0
  6. 2026PCAP-LM: An LLM-Native Text Representation for TLS Bulk Traffic Analysiscited by 0
  7. 2026SoK: Semantic Decision Engines in Network Control Loopscited by 0