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LLMcap: Large Language Model for Unsupervised PCAP Failure Detection

Lukasz Tulczyjew, Kinan Jarrah, Charles Abondo, Dina Bennett, Nathanael Weill

TL;DR

This paper tackles scalable PCAP failure detection in telecom networks under limited labeled data. It introduces LLMcap, a self-supervised, masked language modeling pipeline that learns PCAP grammar from unannotated data and supports failure localization through chunk-level predictions aggregated to PCAPs. Key contributions include a dual PCAP representation approach (text vs key-value dictionaries), a Failure Detection Algorithm combining thresholding and an unsupervised Elliptic Envelope, and extensive analysis of generalization to external services and execution time. The approach enables efficient, edge-friendly failure detection and offers interpretability by tagging high-signal chunks for root-cause analysis, with future work expanding service coverage and real-time deployment capabilities.

Abstract

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.

LLMcap: Large Language Model for Unsupervised PCAP Failure Detection

TL;DR

This paper tackles scalable PCAP failure detection in telecom networks under limited labeled data. It introduces LLMcap, a self-supervised, masked language modeling pipeline that learns PCAP grammar from unannotated data and supports failure localization through chunk-level predictions aggregated to PCAPs. Key contributions include a dual PCAP representation approach (text vs key-value dictionaries), a Failure Detection Algorithm combining thresholding and an unsupervised Elliptic Envelope, and extensive analysis of generalization to external services and execution time. The approach enables efficient, edge-friendly failure detection and offers interpretability by tagging high-signal chunks for root-cause analysis, with future work expanding service coverage and real-time deployment capabilities.

Abstract

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.
Paper Structure (22 sections, 2 equations, 4 figures, 5 tables)

This paper contains 22 sections, 2 equations, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Formulation
  4. Proposed System/Model
  5. Data Collection
  6. PCAP Parser and Preprocessor
  7. Parsing
  8. Data Sanitization
  9. Chunking
  10. Masking
  11. Large Language Model Training
  12. Model Selection: DistilBERT and Knowledge Distillation
  13. Loss Function and Training Configuration
  14. Input Token Masking and Shuffling
  15. Experimental Setup
  16. ...and 7 more sections

Figures (4)

  • Figure 1: The overall strategy for training and inference using LLMcap.
  • Figure 2: Example of formatted original and masked input to MLM training of LLM model.
  • Figure 3: The visual representation of two measures, i.e., the number of misclassifications NOM-K and mean NLL-K loss, for ground-truth labels (top), and predictions of our method (bottom).
  • Figure 4: Listing shows an example of output from LLMcap. In this case, the model found 2 sections of SIP-related errors.