Efficient Generation of Specialized Large Language Models for Network Traffic Analysis

Link to the online patent PDF

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Overview

This patent describes a groundbreaking approach to network traffic analysis by leveraging Large Language Models (LLMs). Instead of building separate AI models from scratch for each network analysis task, the invention proposes training a single base LLM on network traffic capture files (PCAP files) and then using transfer learning to rapidly create multiple specialized LLMs for different applications. Each specialized model can perform specific network analysis tasks, such as anomaly detection, failure prediction, knowledge graph generation, or error diagnosis, by building upon the foundation established by the base model.

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The Problem

Network administrators rely heavily on PCAP files to diagnose network issues, detect anomalies, and troubleshoot errors. However, traditional methods of analyzing these files are labor-intensive, time-consuming, and error-prone. They require:

• Manual examination of raw packet data by skilled personnel
• Significant computational resources and time
• Pre-trained models that may not accurately capture the specific nuances and characteristics of a particular network environment
• Human intervention for model adaptation and fine-tuning

Moreover, building specialized AI models for each network analysis task (anomaly detection, failure prediction, root cause analysis, etc.) from scratch is inefficient and redundant, as these tasks share common patterns in network traffic data.

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The Solution

The patent proposes a two-stage training framework:

1. Base Model Training: A base LLM (using architectures like BERT) is trained on network traffic capture files using masked language modeling. The model learns to predict masked portions of PCAP data, effectively learning the patterns, protocols, and structures of normal network traffic. This foundational training uses only successful call flows (no errors), teaching the model what "normal" network behavior looks like.
2. Transfer Learning for Specialization: From this single base model, multiple specialized LLMs are created through transfer learning techniques (fine-tuning, LoRA, QLoRA, adaptors, etc.). Each specialized model is trained with a smaller, task-specific dataset to perform a particular network analysis function:
   • Anomaly Detection: Masks portions of incoming traffic and flags anomalies when prediction accuracy drops below a threshold
   • Failure Detection: Identifies specific error types in network communications
   • Knowledge Graph Generation: Extracts entities (protocols, error codes, network elements) and their relationships
   • Error Prediction: Generates call flow descriptions and predicts root causes of errors
   • Packet Generation: Creates synthetic network packets for testing and augmentation
   • Continuous Reporting: Summarizes network operating parameters over time

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Why It Matters

This architecture represents a paradigm shift in network traffic analysis:

• Efficiency: Instead of training multiple models from scratch, one base model is trained once, and specialized variants are created quickly through transfer learning with smaller datasets.
• Scalability: New network analysis applications can be developed rapidly by adapting the base model, reducing time-to-deployment from months to days or weeks.
• Performance: The base model captures deep patterns in network traffic that specialized models can leverage, potentially improving accuracy compared to task-specific models trained in isolation.
• Resource Optimization: Transfer learning requires significantly less training data and computational resources compared to training models from scratch.
• Automation: These specialized models can automate complex network analysis tasks that traditionally required expert human analysis, enabling real-time network monitoring and response.

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Relevance Beyond Telecommunications

The principle of training a foundation model on domain-specific data and then creating specialized variants through transfer learning has broad applicability:

• Cybersecurity: A base model trained on security logs could spawn specialized models for intrusion detection, malware classification, vulnerability assessment, and threat intelligence analysis.
• Healthcare Systems: Train a base model on electronic health records (EHRs) and system logs, then create specialized models for patient flow optimization, equipment failure prediction, data breach detection, and compliance monitoring.
• Industrial IoT and Manufacturing: A base model trained on sensor data and machine logs could generate specialized models for predictive maintenance, quality control, production optimization, and supply chain monitoring.
• Financial Transaction Monitoring: Train on transaction logs to create specialized models for fraud detection, regulatory compliance, market manipulation detection, and customer behavior analysis.
• Cloud Infrastructure Management: A base model on cloud system logs could spawn specialized models for cost optimization, performance monitoring, security incident detection, and capacity planning.

The key insight is that any domain with complex, structured log or trace data can benefit from this approach, building a strong foundation model that understands the domain's "language" and then efficiently creating specialized tools for specific analytical tasks.

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Technical Details

The system architecture includes several key components:

• Base Large Language Model (316): Built on transformer architectures (e.g., BERT), consisting of:
  • Embedding Module (412): Converts PCAP data into contextual embeddings with positional information
  • Encoder Stack (406A-406N): Multiple encoder layers, each containing:
    • Multi-Head Attention (418): Captures relationships between different tokens in the network traffic data
    • Feed Forward Networks (426): Applies transformations to the attention output
    • Add & Norm Modules (422, 430): Residual connections and layer normalization for training stability
• Training Components:
  • Main Training Data Storage (310): Stores PCAP files (primarily successful call flows) for base model training
  • Base LLM Trainer (314): Implements masked language modeling to train the base model on network traffic patterns
  • Supplemental Training Data Storage (340): Stores smaller, task-specific datasets for transfer learning
• Model Adaptor (318): Orchestrates the creation of specialized models through:
  • Scheme Selector (618): Selects appropriate transfer learning technique based on the task
  • Trainer (622): Performs additional training with task-specific data
  • Evaluator (626): Assesses model performance and triggers modifications if needed
  • Module Storage (630): Contains reusable modules (preprocessing, postprocessing, specific layers) that can be added to specialized models
• Specialized Applications (350A-350X): Each contains a specialized LLM (352A-352X) for specific tasks:
  • Anomaly Detector (802): Uses misprediction aggregation to identify network anomalies
  • Failure Detector (902): Classifies network failures into specific error categories
  • Knowledge Graph Generator (1000): Extracts entities and relationships from network traffic
  • Error Predictor (1050): Uses cascaded LLMs to generate call flow descriptions and predict root causes
  • Packet Generation Model (1106): Creates synthetic network packets for testing and training
  • Extraction Model (1206): Generates periodic reports summarizing network parameters

Status: Published Application Number: 18/524,850 Publication Number: US 2025/0184247 A1 Filing Date: November 30, 2023 Publication Date: June 5, 2025 Inventors: Lukasz Tulczyjew, Nathanael Weill, Charles Abondo, Albert Khoury Aouad

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