The ingest layer determines the ontology, and the ontology determines whether the hypergraph becomes a sparse skeleton or a living, operator‑grade organism.

Scapy is phenomenal because it gives you raw packet material and full protocol dissection control, but it’s only one member of a much larger ecosystem of tools that can mint the rich entity types your HypergraphEngine thrives on.

Below is a curated set of tools—not packet analyzers, but entity‑emitters—that can feed your geographically contextual hypergraph with the flow nodes, port hubs, TLS certs, DNS names, HTTP hosts, and service fingerprints you listed.

Tools That Can Feed a Geographically Contextual Hypergraph

🛰️ 1. TShark / Wireshark CLI

The CLI version of Wireshark is a hypergraph goldmine because it can emit structured fields directly:

tshark -T fields -e ip.src -e ip.dst -e tcp.srcport -e tcp.dstport
-e tls.handshake.extensions_server_name
-e dns.qry.name
-e http.host

This gives you flow nodes, port hubs, SNI nodes, DNS qname nodes, HTTP host nodes, etc.

It’s essentially a packet → graph primitive compiler.

🧬 2. Zeek (Bro)

Zeek is the closest thing to a hypergraph-native ingest engine that already exists.

It automatically emits:

conn.log → flow nodes, service fingerprints
dns.log → dns_name nodes
ssl.log → tls_cert nodes (issuer, subject, fingerprint, SNI)
http.log → http_host nodes, user-agent nodes
files.log → file-hash nodes
geoip integration → host → geo edges

Zeek is basically a graph primitive factory.

If Scapy is a scalpel, Zeek is a full surgical suite.

🧭 3. Suricata

Suricata’s EVE JSON output is perfect for hypergraph ingestion.

It emits:

Flow metadata
TLS certs (fingerprints, issuers, SNI)
DNS queries/answers
HTTP hosts, URLs
JA3/JA3S fingerprints
GeoIP metadata

You can wire EVE JSON directly into your GraphEventBus and mint nodes on arrival.

🌐 4. Nmap + NSE Scripts

Nmap is not just a scanner—it’s a service ontology generator.

It can emit:

Service nodes (ssh, http, rdp, smb, etc.)
Version nodes (Apache 2.4.57)
Script-derived nodes (TLS certs, SMB domains, HTTP titles)
Port hubs (tcp/22, tcp/443, udp/53)

Nmap + NSE is a graph enrichment engine.

🧩 5. Masscan + ZMap

For large-scale topology discovery:

Masscan → port hubs + host nodes
ZMap → internet-wide flow skeletons
ZGrab → TLS certs, HTTP hosts, banners

This is how you go from local topology to planetary topology.

🔍 6. Mitmproxy

For environments where you can intercept traffic:

HTTP host nodes
URL nodes
Cookie nodes
TLS cert nodes
Flow nodes with full request/response metadata

It’s a hypergraph emitter for application-layer semantics.

📡 7. PyShark

A Python wrapper around TShark that gives you structured packet fields without writing dissectors.

Useful when you want Scapy-like ergonomics but Wireshark-grade protocol coverage.

🧠 8. Passive DNS (Farsight, SecurityTrails, DNSDB)

For DNS‑centric hypergraphs:

dns_name → ip edges
historical edges
TTL-based temporal edges
authoritative NS nodes

This gives you temporal topology, not just spatial.

🛰️ 9. MaxMind GeoIP + GeoLite2

You already use host → geo edges, but you can expand:

ASN nodes
ISP nodes
Org nodes
RIR nodes
Country/region/city nodes
Lat/long nodes

This turns your hypergraph into a geospatial lattice.

How These Tools Map to Your Hypergraph Ontology

Entity Type	Scapy	Zeek	Suricata	TShark	Nmap	ZGrab	Mitmproxy
host nodes	✔	✔	✔	✔	✔	✔	✔
geo nodes	(external)	✔	✔	(external)	(external)	(external)	(external)
flow nodes	✔	✔	✔	✔	partial	partial	✔
port hubs	✔	✔	✔	✔	✔	✔	✔
dns_name nodes	partial	✔	✔	✔	✖	✖	✖
tls_cert nodes	partial	✔	✔	✔	✔	✔	✔
http_host nodes	partial	✔	✔	✔	✔	✔	✔
service nodes	partial	✔	✔	partial	✔	✔	✔

Scapy is the artisan tool.
Zeek/Suricata are the industrial emitters.
Nmap/ZGrab are the explorers.
Mitmproxy is the semantic layer.

Together, they give you operator-grade topology explosion.

If you want, I can design:

A canonical ingest ontology for your HypergraphEngine
A unified event schema for all these tools
A graph DSL extension for flows, certs, DNS, HTTP
A 5,000‑node topology explosion pipeline
A geo‑aware hypergraph layout strategy for Cesium or WebGL

Just tell me which direction you want to push next.

In other news:

Hugging Face hosts a growing ecosystem of datasets, models, and tools focused on Radio Frequency (RF) In-phase and Quadrature (IQ) signal analysis, enabling the application of AI/ML to wireless communications, such as modulation classification, device fingerprinting, and signal identification. [1, 2, 3, 4, 5]

Key Hugging Face RF IQ Resources

Datasets: Hugging Face contains datasets with raw RF IQ signals. Example datasets include and various datasets (e.g., , ).
RF-Lang Benchmark: A dataset providing a direct, structured link between raw RF I/Q signals and natural language supervision, designed for joint RF-language understanding.
Models: Research in this area utilizes deep learning models (CNNs, Transformers) to process IQ data for tasks like modulation classification. [2, 4, 6, 7, 8]

Applications of RF IQ on Hugging Face

RF Fingerprinting: Identifying unique hardware imperfections in transmitters using IQ samples, often using deep learning models (CNNs or Transformer-Encoders).
Modulation Classification: Classifying signal types using IQ data or converted imagery (spectrograms).
Wireless Foundational Models (WFMs): Emerging models, such as IQFM, are being developed to process raw IQ streams for diverse tasks like beam prediction and angle-of-arrival (AoA) estimation.
Domain Adaptation: Using specialized representations like Double-Sided Envelope Power Spectrum (EPS) to improve model robustness to varying environments. [1, 3, 9, 10, 11]

Techniques for Processing RF IQ

Complex IQ Data: Raw data consists of complex IQ samples, often represented as real/imaginary traces.
Image Conversion: Converting IQ samples into visually interpretable inputs (e.g., spectrograms) allows for the use of vision-based models.
Attention-Based Fusion: Combining IQ samples with other signal features (like FFT coefficients) via attention mechanisms to improve classification accuracy. [9, 12, 13]

Researchers often use the and libraries on Hugging Face to train and deploy these models. [2, 4, 10, 14, 15]

[1] https://arxiv.org/abs/2506.06718

[2] https://huggingface.co/datasets/Francesco/radio-signal

[3] https://arxiv.org/abs/2511.15162

[4] https://www.ibm.com/think/topics/hugging-face

[5] https://link.springer.com/chapter/10.1007/978-981-97-5609-4_2

[6] https://www.researchgate.net/publication/394671009_RF-Lang_A_Large-Scale_Dataset_for_Grounding_Language_in_Radio-Frequency_Signals

[7] http://www.diva-portal.org/smash/get/diva2:1905507/FULLTEXT01.pdf

[8] https://huggingface.co/datasets?other=rf-signal

[9] https://arxiv.org/abs/2601.13157

[10] https://arxiv.org/abs/2412.10553

[11] https://arxiv.org/abs/2308.04467