Network Packet Capture Analysis Using Machine Learning Model

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Link to the online patent PDF

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Overview

This patent describes an innovative approach to analyzing network packet captures (PCAP files) in telecommunications networks using machine learning models. Instead of relying on manual analysis or simple rule-based systems, the invention proposes a multi-stage ML pipeline that automatically correlates network packets, labels communication flows, and predicts success or failure of network operations. When failures are detected, the system can further analyze the root cause, enabling faster and more accurate network troubleshooting.

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The Problem

Network operators face significant challenges when diagnosing issues in telecommunications networks:

• Manual PCAP Analysis: Network engineers must manually examine packet capture files to identify issues, which is time-consuming and requires deep expertise
• Complex Multi-Protocol Flows: Modern telecommunications involve multiple protocols (4G/5G core, IMS, RAN) working together, making it difficult to correlate packets across different protocol layers
• Delayed Issue Detection: Traditional monitoring tools may not detect failures until they impact multiple users, leading to prolonged outages
• Root Cause Identification: Even when failures are detected, determining the underlying cause requires extensive manual investigation across multiple network domains
• Scalability Challenges: As network traffic grows, manual analysis becomes increasingly impractical

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The Solution

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The patent proposes a sophisticated machine learning framework with multiple stages:

1. Packet Preprocessing & Correlation: The system receives network packets from various points in the network and preprocesses them to:
   • Extract a relevant subset of packets for analysis
   • Correlate packets within the same protocol procedure (e.g., all packets in a registration flow)
   • Correlate packets across different protocols that relate to the same service (e.g., linking 4G core packets with IMS voice call packets)
   • Tag correlated packets with labels indicating success or failure of specific subtasks
2. Feature Extraction: From the labeled and correlated packets, the system extracts features that capture:
   • Timing patterns and sequences
   • Protocol-specific parameters and states
   • Cross-protocol relationships
   • Historical patterns from similar flows
3. Success/Failure Prediction (First ML Model): A primary machine learning model analyzes the extracted features to predict or infer whether the overall communication flow succeeded or failed. This model can output:
   • Success indication
   • Failure indication
   • Unknown status (when confidence is low)
4. Root Cause Analysis (Second ML Model): When a failure is predicted, a second specialized ML model analyzes additional features to determine the specific cause of the failure (e.g., authentication failure, timeout, configuration error, resource exhaustion)
5. Success Type Classification (Third ML Model): When success is predicted, an optional third model can classify the type of success (e.g., voice call, data session, SMS) for more granular analytics

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Why It Matters

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This multi-stage ML approach represents a significant advancement in network operations:

• Automation: Eliminates the need for manual PCAP analysis, enabling real-time network monitoring and issue detection
• Accuracy: Machine learning models can identify subtle patterns and correlations that human analysts might miss
• Speed: Automated analysis provides near-instantaneous diagnosis, dramatically reducing mean time to resolution (MTTR)
• Scalability: Can process massive volumes of network traffic that would be impossible to analyze manually
• Continuous Learning: Models can be trained on synthetic data from network simulators, enabling proactive detection of issues before they occur in production
• Cross-Domain Intelligence: By correlating packets across multiple protocols, the system provides holistic visibility into complex multi-domain network operations

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Relevance Beyond Telecommunications

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The principle of multi-stage ML analysis with correlation and cascaded models has broad applicability:

• Cloud Infrastructure Monitoring: Correlate logs across microservices to detect cascading failures, predict root causes in distributed systems, and classify incident types automatically
• Industrial IoT and Manufacturing: Analyze sensor data streams from multiple machines to predict equipment failures, identify root causes (mechanical, electrical, software), and classify production quality issues
• Financial Transaction Processing: Correlate transaction logs across multiple systems (payment gateway, fraud detection, settlement) to detect anomalies, predict transaction failures, and identify root causes (insufficient funds, network timeout, fraud)
• Healthcare Systems: Analyze patient monitoring data across multiple devices and systems to predict adverse events, identify root causes (medication interaction, equipment malfunction), and classify event severity
• Cybersecurity: Correlate security logs across network layers (firewall, IDS, endpoint) to detect multi-stage attacks, predict breach success/failure, and identify attack vectors and root causes

The key insight is that any domain involving complex, multi-system interactions with sequential or correlated events can benefit from this cascaded ML approach—first detecting anomalies or failures, then drilling down to identify specific root causes.

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Technical Details

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The system architecture includes several key components:

• Network Packet Reception: Collects packets from strategic locations in the telecommunication network (e.g., core network interfaces, RAN backhaul, IMS gateways)
• Preprocessing Engine: Implements sophisticated correlation algorithms:
  • Intra-Protocol Correlation: Links packets within the same protocol procedure (e.g., all packets in a 4G attach procedure)
  • Cross-Protocol Correlation: Links packets across different protocols serving the same service (e.g., connecting 4G bearer establishment with IMS call setup)
  • Labeling Module: Tags correlated packet groups with success/failure indicators for specific subtasks
• Feature Extraction Module: Generates multiple feature sets:
  • First Features: Primary characteristics extracted from labeled, correlated packets
  • Second Features: Subset of first features optimized for root cause analysis
  • Third Features: Another subset optimized for success type classification
• Multi-Stage ML Pipeline:
  • First ML Model: Binary or ternary classifier (success/failure/unknown) trained on first features
  • Second ML Model: Multi-class classifier for failure root cause, trained on second features, activated only when first model predicts failure
  • Third ML Model: Multi-class classifier for success types, trained on third features, activated only when first model predicts success
• Training Infrastructure:
  • Synthetic Data Generation: Uses network simulators to generate training data with known outcomes
  • Threshold Configuration: Different classification thresholds for first and second models to optimize precision/recall tradeoffs
  • Continuous Model Updates: Models can be retrained with new data to adapt to evolving network conditions
• Inference Output Generation: Produces actionable insights including:
  • Success/failure prediction with confidence scores
  • Root cause identification for failures
  • Success type classification
  • Relevant packet sequences and timing information for further investigation

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Status: Published (Notice of Allowance Received) Application Number: 18/353,920 Publication Number: US 2025/0030611 A1 Filing Date: July 18, 2023 Publication Date: January 23, 2025 Notice of Allowance: February 3, 2026 Inventors: Kenan Jarah, Jamal Atieh, Joseph Broumana Majdalani, Mohammad Zakaria, Pierre Moufarrege

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