Follow the tracks
Synthetic vessel trajectories establish the maritime scenario.
Aegis-6G
NETWORKING / FORMAL SYSTEMSAcademic maritime simulation
A track. A handshake. A reason to look closer.
THE CONTEXT
Explore protocol and behavior signals in a simulated maritime network.
THE KEY DECISION
Keep formal handshake validation distinct from heuristic risk and graph context.
THE RESULT
A public command-center interface; live stream availability depends on the backend.
Aegis-6GTRACK → RELATIONSHIP
MECHANISM STUDY / 10SCHEMATIC
TAKE A CLOSER LOOKSELECT A STAGE ↓
SCROLL TO TRACE. SELECT TO EXPLORE.
Follow the connections as you scroll, or select a stage to see what happens there.
Validate & assess
A DFA checks protocol order; fuzzy rules assess modeled behavioral risk.
Connect the context
Ownership relationships supply an additional graph-based explanation.
An academic simulation of maritime spoofing and anomaly analysis in a non-terrestrial-network setting, inspired by the Strait of Malacca.
PROJECT TYPE
Simulation & explainable systems
FOCUS
Networking / distributed + Cybersecurity + AI / ML + Research
STATUS
Academic maritime simulation
/01
The question.
A malformed protocol sequence and an unusual vessel trajectory are different kinds of evidence. Combining them should not erase the difference between formal validation and heuristic risk.
/02
The approach.
A DFA checks a modeled handshake sequence. Mamdani inference and ART1-inspired grouping assess simulated behavior. A NetworkX ownership/syndicate graph provides another relationship layer, delivered through a FastAPI/WebSocket pipeline to a Next.js interface.
/03
What came out of it.
The system makes protocol rejection, behavioral risk and relationship context visible as separate stages. The public command-center interface is available; its backend stream availability is a separate runtime condition.
01
Formal means formal
The state machine accepts or rejects a modeled protocol sequence. It is not a claim of complete implementation of a deployed 6G standard.
02
Risk is a different layer
Mamdani rules and ART1-inspired grouping assess the simulation’s behavior. These scores are not learned real-world detection accuracy.
03
Relationships need provenance
The graph provides synthetic ownership and syndicate context. Its connections are scenario data, not intelligence about real vessels.
Under the surface.
- Protocol
- Python · deterministic finite automaton
- Risk & relationships
- Mamdani inference · ART1-inspired grouping · NetworkX
- Streaming interface
- FastAPI · WebSockets · Next.js · TypeScript
SCOPE & LIMITS
- Academic simulation, not an operational maritime defense product.
- All vessel tracks, risk shapes and relationships shown here are schematic.
- The public frontend can load independently of its backend stream.
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