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Recipient list analysis - Intruder detection

IP.com Disclosure Number: IPCOM000245686D
Publication Date: 2016-Mar-30
Document File: 4 page(s) / 102K

Publishing Venue

The IP.com Prior Art Database


Disclosed is a IT solution to avoid errors when entering a recipient list for an email or instant message system. This idea describes how a software could help user to avoid mistake when typing down this list. The analysis is based on mailbox history and contact list analysis

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 52% of the total text.

Page 01 of 4

Recipient list analysis - Intruder detection

Overview of the idea

Delivering a message to the right list of receivers is the base of any kind of communication, by mail, via social networks, etc...

Today, when we enter our list of recipient, there is a simple validation which is done on the each recipient.

- Surname are automatically replaced by email (if the surname is known in the contact list)

- Incorrect email addresses are highlighted, etc...

    There is no checking done to prevent entering a wrong address in a recipient list. That leaves a real risk to send a message to the wrong person.

    That problem could be solved by analyzing the recipient list with the help of all data available in a related social network or in an address book.

    If a mail is sent to 10 peoples that seems to be highly connected because there are working in the same company, an automated service could detect that this eleventh recipient who is absolutely not in the same "circle" of contact is not a right recipient.

How it works Step by Step
Step #1 - Starting the analysis
Step #2 - Building connection between recipients
Step #3 - Combine all these scores and transform them as warning for the end user. Example: I have the following contact in my contact list
32 years old
SocietyA 35 years old USA

Mike Alpha SocietyA 40 years old USA

Mike Beta CompanyB 38 years old USA

    Problem: I want to send a mail to my colleagues but by mistake, instead of adding Mike Alpha to the list, the "auto completion" feature suggested Mike Beta and I did not noticed this error.

    What will happened with this solution: Here, Mike Beta could be spotted as an intruder by our mechanism because his profile does not look like the other one. And more, our mechanism has already spotted this "risk" between Mike Alpha & Mike Beta because of their common first name.

Page 02 of 4

More detail about step A. Analyze recipient list.


Page 03 of 4

Step #1 - Starting the analysis

    Each time we would want to send a mail or a message on a social network, a service, connected to our profile will analyze the list of contact and build a schema of the connection...