Your Data Zen starts here.

Facebook 1: Private friend list map

Facebook is a great tool to stay in touch, to meet new people and also to find plenty of opportunities for fun or even a solid job.
We all are still searching for optimal way, how to get the most from social networks while keep reasonable level of privacy and security. And of course the decency, in certain cases. We all know that “one person”.. 🙂

Goal of my Facebook studies is to develop reproducible automated way, how to spot malicious activity while keep user´s privacy intact.
I have used plugin for Google Chrome called: Lost Circles, to obtain my own network.

On Facebook it all starts with the friend list.

Are you the king of person, which “adds” only the people of trust, people met previously in person?
Or do you grow your network as possible?

With time, I managed to grow my network nice and easy. Being subject of this study, network is composed from 800 nodes connected by more than 5000 edges:
On picture above, I have spotted couple of communities by marking them with different color.
Each of these stands for a so called “domain” of my (private) life.

For sure you could name similar categories:

  • Childhood / Elementary school friends
  • High school
  • College folks
  • Romantic acquaintances
  • Colleagues from work life
  • People sharing same hobby or interest
  • Family members
  • People you have added for some reason
  • People who added you for some reason

These domains do vary for each user and also the total count of friends on the list is somewhat variable. By the time of writing this article, average number of Facebook “friends” is around 220 – 330 depending on age and gender of the user. On picture below, it is obvious that some of the people are more connected that other people {The Node is bigger – more connected}:

In cyber security research, this is important as more connected people are more valuable target for spear phishing attacks, once they are “owned” they are more valuable for pivoting. In real life, these people are usually quite important for you.
(Those 4 friends average lucky person does have, your significant other, close family member, secret lover..)

Moreover, if you will obtain a suspicious message from them, you would be less cautious than if a complete stranger asks you a favor.

Once basic friend list network domains are identified and populated, next step is to identify the outliers.
{People in the list but not fitting actually to any domain}

For this purpose, I have switched the visualization algorithm. Now the domains are in connected areas while the “outer circle” is composed by outliers only.

Membership to outliers group should raise a suspicion. Some malicious hackers do add themselves using various methods to the lists of a random people. By doing that, their profile seems more legit.

Domain analysis of such network would reveal that there is either too many domains, or no significant domain at all.

This concludes first article about the SNA method used to analyze Facebook friends in the list.

Next step is to collect data from volunteers to generate an anonymous data model.
Do you want to join the volunteers and participate in this research activity?
Feel free to contact me🙂

So far, parameters for Facebook network evaluation were:

  • The domain – area containing number of people with similar background, similar friends or similar interests.
  • Once a profile is out of any domain, it is considered as an outlier.
  • Last but not least the total of Facebook friends in the network might be the trigger as well.

Research dedicated to Facebook will continue to reach its final goal – to develop a sort of plugin for artificial intelligence in form of neural network which would be capable of multidimensional evaluation of users and their networks without breaching any security or privacy limit.

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