TCyberTrust Lab

INDEPENDENT RESEARCH · VIETNAM

Model the system.
Observe the limits.
Question the evidence.

We turn complex engineering questions into interactive models people can explore, explain and challenge.

At the intersection of 3D programming, systems engineering and automotive cybersecurity.

Explore the V2X lab How we work

Open tools. Explicit assumptions. Evidence you can inspect.

OBSERVATION STUDY / 001V2X

One message.
How much can you know?

Illustrative vehicle position claimA vehicle sends a position claim to receiver B. Showing the reference reveals that the actual position can differ from the claim.position messageA / actual positionoffsetClaimed positionB / receiverCONCEPTUAL DIAGRAM · NOT TO SCALE
What is visible?

B receives a position claim. The message alone does not confirm where A really is.

A teaching illustration, not a detector or a real-vehicle experiment.

3D MODELING & VISUALIZATIONSYSTEMS ENGINEERINGAUTOMOTIVE CYBERSECURITY

01 / OPEN WORK

A question you can
explore yourself.

Start with one focused tool. Inspect the assumptions, change the conditions and see what the available information can—and cannot—establish.

PUBLIC MVP · OPEN SOURCE

V2X
Observation Lab

What can a receiving vehicle confirm from position messages alone?

Open the interactive lab
Documentation ↗Source code ↗
01

Learn through a reveal

Compare two worlds with identical messages. Predict, reveal and create your own lesson.

02

Explore observation limits

Adjust position offsets, delays and omissions. Separate the full scene from what B receives.

03

Inspect evaluation evidence

Review supplied binary predictions, examine errors and export a scoped evidence report.

The scenes and bundled sample are synthetic. The evaluation workspace is separate from the 3D model; no AI inference runs in the demo.

02 / RESEARCH APPROACH

Make the model visible.
Keep the claims bounded.

A useful model helps people ask better questions. It also makes clear where its explanation ends.

01

Make it tangible

Use geometry, motion and interaction to explain systems that are difficult to understand from logs or equations alone.

MODEL + VISUALIZE

02

Separate what is known

Distinguish ground truth, received information and assumptions. Show where missing information changes a conclusion.

OBSERVE + QUESTION

03

Leave a reproducible trail

Share source, scenarios and scoped evidence so others can inspect the work, repeat an example and challenge its interpretation.

REPRODUCE + REVIEW

DIRECTION OF TRAVEL

From visual learning toward structured evidence review for security and AI assurance. Broader assurance workflows remain a research direction—not a claim of deployment readiness.

03 / THE PERSON BEHIND THE LAB

Built from practice.
Driven by curiosity.

Thao Dang

Independent automotive security research

Connect on LinkedIn ↗

TCyberTrust Lab is an independent research initiative connecting a background in 3D programming and education with systems engineering and cybersecurity.

My work starts with a practical question: how can we make a complex system understandable enough to examine its assumptions, test an idea and explain what the evidence actually supports?

9 years

3D programming
and training

5 years

Research and systems
engineering projects

3 years

Exploring and practicing
cybersecurity

Experience across overlapping disciplines; these periods are not additive.

Alongside technical work, I serve as Community Manager for Women in Tech Vietnam. Teaching and community exchange shape how I build tools.

This is a personal research initiative. The work presented here does not represent an employer or imply organizational endorsement.

04 / LET’S COMPARE NOTES

Have a question worth
making visible?

I welcome conversations with educators, security researchers and engineers about clearer models, useful lessons and reproducible evidence.

Start a conversation Share feedback on the tool Explore GitHub ↗