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Human Firewall - IoT 2019 Sapienza

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

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Human Firewall - IoT 2019 Sapienza

  1. 1. Human Firewall Andrea Fioraldi, Leonardo Sarra, Mattia Nicolella
  2. 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. 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. 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. 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. 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. 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. 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. 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. 10. Challenges - Photo retrieval - Facial recognition and profiling - User notification - Feedback collection See you at the MVP’s presentation...
  11. 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/

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