I want to say I come from Norway and there is a lot of people. So
coming all the way from Norway to bring a touch of pin for you.
I just completed my master thesis at MITS. The
administration just switched it to last let's because someone
created a lot of T-shirts so I work at a company called -- and
I am full time developer. So let's get started. At first.
Everyone knows that humans are predictable and when you talk
about security that is kind of not a good thing. So when you
look at -- lost audio -- and you cannot use the same strategies
and a passwords so just to give a recap with pin codes there are
codes are patterns does the
android pattern have? That is bigger number than the pin code
and the interesting thing is the numbers for you -- when you go
into -- when I started this project, I started to think
about how people created their pin codes and the passwords as a
set of using names and names and date of birth but the android
catalog you can't use those in. So what do you do to predict
people's patterns so I start add data and additional data about
the patterns or the users that created the patterns. So created
a pattern. If you look you can guess I think it was kind of
funny because the people that I used the ALP that is dark color,
and the light color is the people that I have not used the
ALP before. As you see the numbers that the reaction time.
How fast the response where creating the patterns and as you
see for the smart phone people that created a patterns have
used the pattern lock before. Responding very quickly. You can
guess what's or why they were giving the patterns. So
predictable. What factors can you look at when you predict
someone pattern? You have two hands and you might be right or
left handed. Writing direction. And your phone has a screen
size. You have difference in gender and age. I want to say
something about the complexity because the pattern lock is not
the same as another other pattern lock or pin code you
cannot just code a pat learn lock based on the number of
corrective notes. So I used the math formula to give each
pattern a score just how visual complex is the pattern. I will
give you example. So the blue ones bot are -- nine but if you
are standing behind someone's back it will be guessing the
first one but the second one is more complex but still the same
number of connections, so what I spot complexity score the smart
phone had the lowest score. Nobody that participated was
above 850 people in the study. And nobody managed to create a
pattern of the maximum score. I was kind of thinking that people
would take it has a challenge when I gave this survey and
people would try to create the most complex pattern. But nobody
managed. So that is kind of the same as with password and pin
codes people are not able to remember or create complex
patterns or password. One of the things that I told you about was
that the pattern you cannot use names and date of birth for
example. But when I started collecting the patterns people
were coming up to me and told me that you know, my mom used her
first letter in her name as a pin code and I was like okay. So
I went through all the patterns and that was possible that
corresponds to a letter in the alphabet and 10. 4 percent of
the data were corresponding to the letter in the alphabet. That
is one in 10. If you pick up a phone one in 10 will have a
pattern corresponding to a letter. That was interesting.
One of the things with the android pattern lock you can
only select one note ones mean that if you knew the starting
point you can reduce the number of possible patterns. I will
give you time to look at that. So in the data set 44 percent
were starting in the upper left corner and if you look at the
green ones you get 73 percent. And if you combine all corners
there is 77 percent chance of pattern starting in a corner.
How predictable. But if you look at everyone in the room there is
only about 10 percent of you that's likely to be left handed
and 90 percent of you are right handed. So I was thinking that
because I look at there is two main ways of creating a pattern
on the screen. You are seeing using one hands or two hands so
if you was one hand you use your thumb to create a pattern or if
you use two hands you use forefingers to enterable with
the screen so if I was left handed, 10 percent of the
population, would it be the same, would I start in the same
corner. So if I holding a left handed person phone would the
pattern or 44 percent of the pattern started on the other
corner on the other side. So for right handed the two pictures
are the starting point starting with one hand and being right
handed and the number looks similar to number I showed you
in the other -- for all the patterns. When looking at left
handed I was disappointed those were not the numbers I was
predicting in the first place. So I was like why, so either
left hand or right hand using one hand or two hands either way
people start on the on the left hand side and in the left hand
corner at first I didn't understand why but then I was
thinking about the reading and writing direction because
research has found if you give a person a set of imagines if you
read from left to right you are scanning the pictures from left
to right so remember the picture from left to right. But people
have another reading and writing direction so reading from left
to right people started scanning the pictures from right to left.
So I didn't get a lot people having another reading and
writing direction but I have a strong belief the reading and
writing direction it will correspond to where you start to
create a pattern. So I will not give you a list of this top 10
patterns and how -- it is more interesting to look at how
people behaver and where you create your patterns. So these
are what is called 3-gram. That is set of three connecting nodes
and in the blue one that is commonly selected 3-grams people
create the patterns sticking to the engineer and it is hard to
show here at this view but lines such as you know you can go the
upper left corner to the bottom. Such lines are really few of the
patterns having a line crossing from the left to the middle of
the bottom. The lines are straight and connected to the.
Next node. So funny to look at the length of the pattern. I
collected the gender of the participants. And I don't know
if it is just you male participants preferring length
or woman being less thinking less about security. I don't
know. Just to show a average length there is distribution of
length selected and the upper -- the graph on the top is for male
and bottom is for female and as you can see the bottom graph has
a high percentage of select patterns of lower lengths. You
see that patterns of length eight, there is really few
patterns of length eight. In the data set there was average 4
percent of the patterns have a length eight. If I show the
calculation of how many patterns of different lengths but there
is 140 --> 000 patterns starting with the length eight. And when
looking at the age of the participants, there were the
younger person the stronger pattern where created for all
the free different patterns. So for bot the shopping and bank
account the younger the people were the -- lost audio -- so by
using this, this is kind of the brief introduction to all the
results that were found. I hope in the future to use the data as
far as I have know there is a mold l because I only have 3400
patterns and how can we be about to predict every pattern first
don't have the pattern in my data set. So by using there is
already -- I have a marker model predicting that has been
developed by a German researcher. And that can predict
the likelihood of the pattern being selected based on the
3-grams that I showed you. So using the predictable ways of
creating patterns likely hood of selecting a pattern can be
estimated and in the future, the use this information that I had
to reduce the number of patterns, to be able the add the
information about the creators to see if we can kind of get a
better model of being able to predict a pattern based on the
person or who the person is. So for know there is about two or
three minutes left so I might be able to take a couple of
questions before I leave. I will stand here if someone want to
talk and ask question.