Over the past few weeks, I’ve been developing PacketMind, an open-source framework that enables AI assistants to reason directly over network packet captures instead of treating them as opaque files.
The idea started with a simple question:
Why are engineers still manually scrolling through thousands of packets to understand a protocol workflow?
Today, PacketMind can ingest PCAP traces and automatically extract structured signaling timelines across multiple telecom protocols, including:
• NAS-5GS
• NGAP
• S1AP
• RRC
• Diameter
• PFCP
• GTPv2
Instead of producing raw packet dumps, the system generates machine-readable event sequences such as:
UE → AMF → SMF → UPF interactions
Registration procedures
Authentication exchanges
Session establishment flows
Mobility events
Protocol state transitions
This is accomplished through an MCP-based architecture that connects AI agents directly to Wireshark’s TShark engine, enabling protocol-aware reasoning rather than simple text summarization.
However, I believe the larger opportunity extends far beyond telecom.
Many of the challenges encountered in 5G and LTE packet analysis also exist in:
• Space communications
• Mission telemetry systems
• Ground station networks
• Distributed autonomous systems
• Satellite communication architectures
• Deep-space data pipelines
The long-term vision is to create a platform where AI systems can automatically transform low-level communication traces into higher-level operational understanding.
Imagine an AI agent that can:
Reconstruct protocol state machines automatically
Identify signaling anomalies before service degradation occurs
Build knowledge graphs from packet-level interactions
Generate sequence diagrams from raw telemetry
Detect previously unseen communication patterns
Explain failures using protocol context rather than statistical alerts
Assist engineers in debugging complex distributed systems
Current Architecture:
PCAP → Protocol Extraction → Structured Events → AI Reasoning Layer → Sequence Diagrams / Analysis / Knowledge Graphs
What I Need Help With
I’m looking for contributors interested in:
-
AI/ML for networking and protocol analysis
-
Graph neural networks for communication systems
-
State-machine inference and protocol modeling
-
Knowledge graph generation from temporal events
-
Space communication protocols and telemetry pipelines
-
Agentic AI workflows for engineering systems
-
Explainable AI for operational networks
-
Dataset creation and benchmarking
Potential Research Directions
• Protocol Foundation Models
• Telecom-to-Telemetry Transfer Learning
• AI-Assisted Root Cause Analysis
• Autonomous Protocol Debugging Agents
• Temporal Graph Learning for Communication Systems
• Multi-Agent Network Operations
• Space Network Digital Twins
This is currently an open-source effort, and I would love to collaborate with students, researchers, network engineers, AI practitioners, and members of the NASA OSDR AI/ML Working Group who are interested in advancing AI for complex communication systems.
If any of these research directions resonate with you, I’d be grateful for your feedback, ideas, critiques, or contributions.
GitHub Repository:
What research direction would you prioritize first, and why?
I would also appreciate any thoughts from @rtscott2001 and others who are working on AI methods for complex scientific and operational data.
#AI #MachineLearning #AgenticAI #Networking #Telecommunications #5G #ProtocolEngineering #Wireshark mcp #KnowledgeGraphs #GraphNeuralNetworks #SpaceCommunications telemetry nasa osdr #OpenSource research