User-centered presentation of the Human Firewall project at Sapienza Univeristy

1. Human Firewall
Andrea Fioraldi, Leonardo Sarra, Mattia Nicolella

2. The user target
- The user lives in his own house and has an internet connection
- Has, at least, a bare knowledge of how a messaging app works
- The user may or may not have a business activity
Frustrations
Doesn’t like unwanted people on his doorstep or in his shop like
scammers, religious people doing proselytism or rude customers.
He is willing to participate in a project to profile those unwanted people
Task

3. Idea - Problem Space
- Dealing with scammers at doorstep, also only for saying “go
away”, is a waste of time especially for people that are working
- Would be nice if the community can review each unknown people
at the doorbell and report scammers faces to warns the user
directly on the smartphone

4. Our solution: an intercom
- Always connected to the internet
- Take photo and profile the person that
rings the bell
- Trigger a notification to the
house/business owner

5. Environment
- It can be used in user’s home or
business activity
- Product is placed at the doorstep and
can replace the normal intercom
- Smart home/building oriented

6. Scenarios
- We want to know if a client in our shop is known for unpleasant
behaviors by other merchants and then not assign
salesmen to him.
- We want to know if the person at our doorstep is a known
scammer or an unwanted person.
Weaknesses
- Incorrect classifications can discourage clients to buy or
cause a time loss.
- We need a legal note about the profiling

7. User interaction
- User is notified in real-time on
his phone about the identity and
reputation of the person that rang the
intercom (when available)
- User is requested to provide
feedback about the
classification
- Or manually profile a person that
wasn’t already profiled before

8. Similar solutions
Smartphones from Google and Samsung uses a blacklists of
known scammers numbers. This is also done (under payment)
by operators like AT&T and by third-party apps with a community-based
reporting systems.
We want to bring this to real life.

9. Initial Feedbacks
We got some initial feedbacks from people part of the infosec
community.
We used twitter and also asked direct feedback to potential users that
usually work / play CTFs at home and have this problem with scammers
and pests.
After that, many people are encouraging us to go forward with
this project!

10. Challenges
- Photo retrieval
- Facial recognition and profiling
- User notification
- Feedback collection
See you at the MVP’s presentation...

11. Info & Contacts
GitHub organization: https://github.com/humanfirewall-iot19
Andrea contacts:
● Twitter: @andreafioraldi
● GitHub: https://github.com/andreafioraldi
● LinkedIn: https://www.linkedin.com/in/andrea-fioraldi/
Leonardo contacts:
● GitHub: https://github.com/LithiumSR/
● LinkedIn: https://www.linkedin.com/in/leonardo-sarra-b34691182/
Mattia contacts:
● GitHub: https://github.com/Nick1296
● LinkedIn: https://www.linkedin.com/in/mattianicolella/