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International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 1
Mobile user authentication based on user behavioral pattern
Hassan Sbeyti email@example.com
Faculty of Computer Study
Arab Open University, Omar Bayhoum Street,
Beirut, 2058 4518, Lebanon
Smart devices are equipped with multiple authentication techniques and still remain prone to
attacks since all of these techniques require explicit user intervention. The purpose of this paper
is to capture the user behavior in order to use it as an implicit authentication technique.
In this paper, we introduce a novel authentication model to be used complementary to the
existing models; Particularly, the context of the user, the duration of usage of each application
and the occurrence time were examined and modeled using the cubic spline function as an
authentication technique. A software system composed of two software components has been
implemented on Android platform. Preliminary results show a 76% accuracy rate in determining
the rightful owner of the device.
Keywords: Security, implicit authentication, behavioral modeling.
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016
The technological advances in all domains are making the use of smart devices in everyday life
more imposing. These range from smart phones to laptops, tablets and even i-watches. This field
is in continuous development and every newly released generation is opening new possibilities to
the engagement with the user's context and increasing security threats. The European Union
Agency for Network and Information Security  listed in a survey the top ten security information
risks for smart phone users. The number one was data leakage resulting from device loss or
theft. This result was also featured by the US-CERT (United States Computer Emergency
Readiness Team), which also mentioned that the number of new vulnerabilities has jumped 42%
from 2009 to 2010.
In order to fight that, smart devices are usually equipped with three authentication factors:
something you know, something you have, and something you are. What you know comes as the
main security recommendation for any user; that is to set up his phone with a pin or a strong
password. But even that level of security can be trespassed if an attacker has enough time and
access to the device. From the user's perspective, that type of authentication has a very low
usability therefore a user might choose to store his password on the device for easier access and
by that compromising its security. Something you have is by proving possession of something
external to the system. Common choices for proving possession are: hardware tokens that
generate one-time passwords, access to an e-mail address, the mobile device itself can be
registered with an application, and then, possession of the device can be used as a something
you have authentication factor. Choices for something you know that require a user to carry an
additional device are less convenient for the user. One of the reasons for the popularity of mobile
device is its convenience. The something you are factor uses biometrics to authenticate users.
Biometric based techniques are multiple such as keystroke analysis that was discussed in a
research published in the International Journal of Information Security in 2007. This paper
identified two typical handset interactions, entering telephone numbers and typing text messages.
It was found that neural network classifiers were able to perform classification with average equal
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 2
error rates of 12.8%. Based on these results, the paper concludes proposing a flexible and robust
framework to permit the continuous and transparent authentication of the user, thereby
maximizing security and minimizing user inconvenience, to serve the needs of the insecure and
functional mobile handset. Also, in 2009, a paper was published discussing a different form of
keystroke dynamics with the finger pressure . This finding has shown that, the finger pressure
gives the discriminative information more than keystroke dynamics with the k-NN analytical
method. Moreover, using only the finger pressure produces high accuracy of a 99% rate.
Combining multiple biometrics may enhance the performance of the personal authentication
system in accuracy and reliability. In Combining fingerprint and voice print biometrics for identity
verification: an experimental comparison , 13 combination methods were compared in the
context of combining the voice print and fingerprint recognition system in two different modes:
verification and identification. The experimental results show that Support Vector Machine and
the Dempster-Shafer methods are superior to other schemes.
These authentication methods have proven their weakness in terms of usability and also
efficiency. These methods are represented in the phones in the form of different screen lock
mechanisms. From these mechanisms, we can name a few, such as:
!!A simple swipe, which does not provide security at all and is simply used as a screen
!!Face unlock where the user provides a shot of his face that is then recognized by the
device and used to unlock it. This method has proven its weakness and its incapability of
recognizing the user if the surrounding conditions of light mainly do not match the ones
on the day he saved the settings.
!!Face unlock and voice which combines the facial with the voice recognition. If the user is
found in a place where he cannot raise his voice to the same pitch as the one used when
he set up this security, then the authentication will fail.
!!Pattern which is the most common form of authentication and yet still weak since an
adversary can guess the pattern of the user by simply checking the screen of the phone
in an appropriate angle to see traces of the finger.
!!PIN and password which are considered as a medium to high security is a combination of
numbers or characters chosen by the user and required to be entered at every attempt to
unlock the screen which can become quite annoying.
The above mentioned methods are becoming more and more annoying for the user since he has
to repeat the same action multiple times a day often over 100 times. These types of
authentication are user dependent and require his immediate intervention and input in order to
proceed. And by that, any explicit action can be memorized by an adversary and used to unlock
the device without the owner's consent. Also, once the device is unlocked, the security feature is
deactivated even if it was not with its rightful owner.
Therefore, an additional layer of security is required, one that does not require direct user
intervention, but works implicitly and continuously to decide whether the user is indeed the
authorized one. The proposed system aims at reducing the number of explicit authentication. Its
purpose is not to replace the common authentication methods, but rather to complement them.
That is, the user can still use his chosen authentication method, but once the phone is unlocked,
the implicit authentication takes charge to determine if the user is indeed the owner or an
In order to be able to decide that, the device has to gather user centric data that will uniquely
characterize the owner. As an example of such data is the gestural input. In the paper Biometric-
rich gestures: a novel approach to authentication on multi-touch devices , a comprehensive set
of five-finger touch gestures was defined, based upon classifying movement characteristics of the
center of the palm and fingertips, and tested in a user study combining biometric data collection
with usability questions. Using pattern recognition techniques, a classifier was built to recognize
unique biometric gesture characteristics of an individual. 90% accuracy rate was achieved with
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 3
single gestures, and significant improvement was noticed when multiple gestures were performed
in sequence. User ratings aligned well with gestural security, in contrast to typical text-based
Another implicit authentication technique discussed in "Implicit user re authentication for mobile
devices"  included the observation of user-specific patterns in file system activity and network
access to build models of normal behavior. The proposed system was able to distinguish
between normal use and attack with an accuracy of approximately 90% every 5 minutes and
consumed less than 12% of a typical laptop battery in 24 hours.
The main focus of our study is to extract the behavior to transform it into a biometric signature
that can be used to authenticate the user. We will attempt to discover whether it is possible to
extract unique user signature from the behavioral pattern to be used as an implicit authentication
mechanism. What kind of user centric information (and in what frequency) should be collected in
order to detect the user behavioral pattern? How to transform the detected pattern into a unique
signature? What correlation methodology should be used to verify the extracted signature?
In this work, we lay foundational work for implicit authentication through the capture of a user's
unique behavioral pattern. The proposed system aims at reducing the number of explicit
authentication. Its purpose is not to replace the common authentication methods, but rather to
complement them. That is, the user can still use his chosen authentication method, but once the
phone is unlocked, the implicit authentication takes charge to determine if the user is indeed the
owner or an attacker. To achieve this, we introduce a technique by which we capture the
signature of the application usage of a user. First, we collect application related data and in
particular the duration of use. Next, we use a mathematical algorithm that will convert that data
into a function particular to this user. This function will be used at run-time to determine if the user
is indeed the rightful owner or an attacker. Our findings support that this is an approach with great
potential. Thus, the main contribution of this work is a framework that helps us understand the
user behavior and transform it into a unique signature that can be used to authenticate the user.
The study provides an insight into quantifying user behavior and using it as a comparison
standard. The remaining parts of this report are organized as follows: Chapter II introduces the
related work. Chapter III presents in details the different components of MOUBE (Mobile user
authentication based on user behavioral pattern), the behavioral pattern extraction and the
mathematical model (cubic spline interpolation). Chapter IV presents the experimental results that
evaluate the proposed model. Finally, chapter V, gives an overview about future work.
Implicit authentication is a very broad topic and has been discussed by multiple papers. We will
first look into the phone recognition; next we will go through some research concerning the user
recognition. These researches are divided between looking into the behavioral pattern of the
user, the keystroke analysis, and the gait recognition. This work is an extension of the work
started by B. Elhajj ,H. Sbeyti (MUSEP) that is based on the same method to generate the
user behavior but the MOUBE system differ form the previous work by the following feature.:
1.!MOUBE is implemented on an android platform; hence it is tested in real condition, where
MUSEP uses simulation.
2.!MOUBE uses two learning phases to generate a dynamic threshold for every hour for
each user, while MUSEP uses a static threshold for all users.
3.!Within MOUBE, the decision whether the user is genuine or intruder is based on five
previous threshold comparisons, in MUSEP one comparison leads to the final decision.
4.!To evaluate accurately the MOBUE system, the number of user tested is three times
more than the number used in MUSEP.
When discussing a pattern of usage, the user is the first thing that comes to mind. However, the
phone itself can present a pattern of usage that would make it detectable. The paper "Who do
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 4
you sync you are? Smartphone Fingerprinting via Application Behavior"  tackles that subject in
particular. The research looks into the timing and data volume of network traffic generated by a
device. They relied on traffic generated by applications such as Facebook, WhatsApp, Skype,
Dropbox, and others. For each packet generated by these applications, they recorded the arrival
time, the size of the packet, and the direction whether it’s an incoming or outgoing packet. Also,
they analyzed the burst which represents the peak of data transfers from the same type of
connection, for example TCP packets. By using the K-NN classifier, they extracted what they
called "fingerprint" of the phone. Following multiple experiments, they concluded that in about 15
minutes, the phone can be recognized with more than a 90% accuracy rate.
2.2!Authentication mechanisms controlled by the phone
Today's mobile devices are equipped with multiple sensors making them prone to attacks. The
researches in the past decade have been guided towards improving their security measures and
authentication mechanisms. In order to be considered as a "smart" device, Fisher et al. 
debate in their paper "Smartphones: Not smart enough?" the idea that a phone should be able to
scale up or down its authentication mechanisms based on contextual information received from
the device sensors. And by that, the phone would be able to assess the risk and match the
corresponding authentication mechanism. First, the paper defines high and low risk scenario
where the high risk represents the public use of credit card information and the low risk such as
saving passwords onto personal devices in order not to enter them at each sign in. Next, they
describe four device context with examples on how the device should behave in low and high risk
scenarios. For example, the device unlock is a common procedure available in all smart phones.
After unlocking our device, we have access to all personal information, except those protected by
an extra layer of password security. In a high risk scenario, the context-aware device should at
first sense that the user picked up the phone and is moving it towards his face. Then, it should
turn on the camera and scan his face for facial recognition to confirm that it is indeed the owner.
Next, it should scan for any known Wi-Fi or Bluetooth devices nearby to determine the user's
location and assess using the microphone also, if the user is in a crowded space. In a low risk
scenario, the phone would just unlock once it recognizes the user's face. Anyone who attempts to
unlock to device other than the legitimate user, would have his photo taken and saved within the
device. The collection of such data would raise privacy concerns, therefore, the future work will
look into minimizing the amount of data collected and aggregate any stored data. Also, they will
attempt to understand how mobile device users construct a mental threat model in a variety of
contexts and incorporate physical world factors into contextual threat models.
User implicit authentication can be achieved by looking into the behavioral pattern of the user. In
2009, in the Palo Alto research center, a paper was published on this same topic. This research
 introduces the notion of implicit authentication, the ability to authenticate a user to its device
based on common actions that the user performs. This paper focuses on the use of this type of
authentication for Mobile Internet Devices in particular. Not omitting the fact that implicit
authentication can be used in a multitude of other fields such as computers, medical devices to
help preserve patients medical records, military equipment and out-of-band transaction
verification. This paper evaluates a technique to compute and maintain an authentication score
based on recent activities of the user. The scoring varies depending on a set of positive and
negative events and depending on the time elapsed. A positive event is defined as a common
habit of the user, and when that occurs, the score increases. A negative event is a non-common
event for the user, when that occurs, the score decreases. Time elapsing decreases the score if
during that time, the user has usually high activity. When the score goes below the event-specific
threshold, explicit authentication is needed by the user in order to access that feature of his
device. The different data sources that can be used to make authentication decisions are grouped
into 3 types: device data, carrier data and third party data. The device data is any data provided
by the phone itself such as GPS coordinates, WiFi/Bluetooth connectivity, application usage,
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 5
biometric-style measurements such as keyboard typing pattern and voice data. The carrier data
can be used to know the user's approximate location and phone call patterns. The third party data
such as cloud services can also be used since an increasing number of applications are hosted
online. The architecture of the implicit authentication model will be as follows: past behavior will
be the key for the learning algorithm, then based on the user model, and recent user behavior, a
scoring algorithm will compute a final score based on which it will be decided whether the user is
the original device owner. User modeling assumed in this paper is using independent features,
where for example, a user's location is independent from its phone call log and any other activity.
The data collected to perform this experiment consisted of emails, calls, SMSs, location, contacts,
calendar, tasks, memos, alerts, battery level, (un)holstering, USB connections, power on/off, SD
card removal/insertions. This data was from a blackberry device, over the period of 3 months. In
order to simplify the research, the analysis was done on phone data and location data. Phone
data in particular was analyzed based on the lapse of time since previous call, as for location
data, they used the interactive clustering algorithm to compute clusters of the most frequently
visited locations. The scoring algorithm was applied on this collected data and noticed that the
score decreases to zero during the periods knows as active, and during that specific day, were
not. Another experiment was conducted where an adversary calls a set of unknown numbers
from the user's device, and the score also quickly decreased to zero. As future work, they will
attempt to make use of all features for the scoring, and report results on false positive and false
negative rates, research methods to model the dependence between different features (i.e.,
activities) and research methods to model adversarial behavior.
SenSec  is an application prototype that constantly collects sensory data from
accelerometers, gyroscopes and magnetometers and constructs the gesture model of how a user
uses the device. SenSec calculates the sureness that the mobile device is being used by its
owner. Based on the sureness score, mobile devices can dynamically request the user to provide
active authentication (such as a strong password), or disable certain features of the mobile
devices to protect user's privacy and information security. The experiment started with offline user
classification by asking a set of 20 random volunteer to repeat 5 to 10 times a certain set of
actions, pick up the phone, unlock it, open the email application, lock the phone and return it to
the table. The online user authentication consisted of giving a phone for users for 24 hours with
the SenSec application running on these phones. A sureness score is calculated. If it falls below
a preset threshold while certain operation is performed, an authentication screen will be pop up
asking user to enter a passcode. Next these same phones are given to other participants as a
negative testing stage. As result, user studies show that SenSec can achieve 75% accuracy in
identifying the users and 71.3% accuracy in detecting the non-owners with only 13.1% false
alarms. Also, SenSec bears an average 4.96 seconds detection delay.
Touch me once and I know it's you! Implicit Authentication based on Touch Screen Patterns 
paper introduced the idea of a second authentication level. That is, if an attacker has already
breached the first level of security, in this case, a lock pattern, the implicit authentication should
be able to figure out that the user is an intruder. In order to perform that, the paper suggests to
look into the way the user performs the input given the assumption that the intruder already in
possession of the user's password pattern. The experiment designed in order to test this idea
started by collecting data from 48 users on 4 different locking patterns (horizontal, vertical,
vertical with two fingers, diagonal). The data collected was analyzed using dynamic time warping
(DTW). This algorithm looks for similarities between sets of data and calculates the cost to match
one onto the other. The result is a warp distance that can be used to determine how similar a set
is to a reference set. In this work, a sequence consists of a time series of touch screen data (all
combinations of X-coordinate(s), Y-coordinate(s), pressure, size, time). The reference set is the
one used to identify the owner of the device as a signature of that owner. For each unlock screen,
the reference set was created by taking the first 20 unlocks (each one a single unlock) for each
user. This first round of testing showed some very low accuracy levels. In the best case, the true
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 6
negative rate was 57%. This means that a little bit more than four out of ten attacks would have
been successful. This was strongly influenced by the time duration of the tests, the environment
which was not realistic, and the fact that the participant was informed on how to act with their
devices. In the second part of the paper, a more realistic approach was taken for the test. An
android application was developed and sent to the participants by email along with a specific
pattern that was assigned randomly. For instance, out of the 26 participants, for whom valid
attacks existed, six reached an accuracy of 90% or higher. This second approach increased the
overall accuracy by more than 20%. Overall, it can be stated that using touch screen data to
identify users works to a certain degree. This is supported by the fact that increasing the
threshold for valid authentication attempts improves overall accuracy. As future work, they
attempt to improve accuracy of the results, also they will be implementing a prototype based on
the presented approach that does the calculation on the mobile device to perform another long-
term study based on this application.
Bo, Zhang et Al. feature in their study a framework entitled SilentSense . It consists of
tracking the touch actions of the user and combine them with a movement based biometrics in
order to verify whether the current user is the owner or guest/attacker. This approach showed that
the user can be identified with an accuracy over 99%. For one operation on the device, the
framework could capture multiple information, including: the coordinate on the screen of both
touch down and release; the duration of one interaction; the sensory data from both
accelerometer and gyroscope, the pressure for the finger touching on the screen, and the motion
condition of the user. This detection combination was tested in a static and dynamic scenario. In
the first, they evaluated the performance through three different applications, including Message,
Album, and Twitter. It was noticed that the framework could reach over 80\% accuracy within ten
event observations, and the owner will be judged within 6 observations. As for the dynamic
scenario, the framework collected their processed vertical and horizontal accelerations in the
earth coordinate system and combined them with touch event features. After 12 steps, the
accuracy to identify a guest can achieve 100% and after 7 steps, the accuracy to identify the
owner can achieve 100.
Dividing that kind of data by application seemed to improve accuracy of the results. Looking at
the application alone, it contains user centric data more than the phone itself. The application
"knows best on when to authenticate and how to authenticate" . In this research, the
application developer decides a suitable classifier depending on the type of application. For
example, for a browser, a classifier based on touch input behavior would provide more accuracy
than one with keystrokes data. This application centric approach achieved over 85\% accuracy
rate after 50 training samples.
Classifying movement characteristics of the center of the palm and the fingertips was considered
among the promising authentication techniques . The five-finger touch gestures achieved a
90\% accuracy rate in recognizing an owner based on pattern recognition techniques.
Frank, Biedert et Al. propose a framework Touchalytics  that relies on touchscreen input as
data source. They discussed in their paper the ability to continuously authenticate users based on
the way they interact with the touchscreen of a smart phone. That interaction is typically the way
the user scrolls text on his phone. It includes sliding horizontally over the screen and sliding
vertically over the screen to move screen content up or down. This behavior covers browsing
through images or navigating to next screens, or reading emails or documents or browsing
menus. Every user interacts differently with his phone in this context and can by that be
authenticated according to this particular feature. In order to be able to distinguish between
different users, the paper suggests the usage of two different classifiers k-nearest-neighbors
(kNN) and a support vector machine with an rbf-kernel (SVM). The kNN classifier takes every
new observation (here: a stroke) and locates it in feature space with respect to all training
observations. The classifier identifies the k training observations that are closest to the new
observation. Then, it selects the label that the majority of the k closest training observations have.
SVM generalizes from the observed data, i.e., it forgets the individual observations after training
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 7
and only saves the decision. Experiments were conducted where a set of users are given a text
to read on their phones and their stroke pattern was recorded. Overall, the authentication difficulty
seems to increase with increasing temporal distance to the training phase. The individuals in the
experiment would complain from having to read a long text and gave up half way. Interestingly,
the long-term authentication of the scrolling classifiers is an exception as its median error rate is
lower than for the inter-session authentication. Thereby, depending on the authentication
scenario, there is approximately a 0% to 4% chance that the correct user will be rejected or that a
false user will be accepted. For some scenarios, this error rate is still too high for the system
being directly implemented as is. However, this result demonstrates that touch-based continuous
authentication is feasible.
Itus  is an open-source framework that can be deployed off-the-shelf and that combines
SilentSense and Touchalytics. It provides an application easy to adapt, extensible and with low
Utilizing the physiological and behavioral biometrics along with environmental factors to recognize
the owner of a device is one approach in implicit authentication. Assuming that every person has
his own movement pattern, that is his manner of walking or moving his feet, then it can be used to
authenticate that person. Mobile devices these days are equipped with gait and location sensors
that allow them to track this movement pattern. Using correlation to model the data in order to
identify the user turned out to be more performing than the FFT (Fast Fourier transform) providing
a 7% error ratio with 10% for FFT . Also, the paper Pet: when cellular phone learns to
recognize its owner  used that gait data and applied a different algorithm. Based on the fact
that that data is a time series, they chose a variant of Dynamic Time Warping (DTW) algorithm
called Fast DTW. The purpose is to assume that the phone will attach to its owner so much that it
will be able to distinguish whenever it is being carried by someone other than its owner and take
security measure automatically. Their future work included the actual implementation of the
recognition system based on this technique.
The MOUBE system is composed of two components as depicted in figure1 namely, the learning
component and the authentication component. The learning component is composed of two
parts: the first part (called learning phase one) is responsible for capturing the user behavior (it
runs over one month) and the second phase (it runs over 15 days and its called learning phase
two) aims at calculating the user threshold. The authentication component runs in the background
infinitely, immediately after executing phase 1 and 2 of the learning component. It acts as implicit
authentication system and is activated only when the user finishes using any application.
Run time phase
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 8
FIGURE 1: MOUBE architecture.
3.1!The Learning component
The learning component is composed of two parts: the first part aims at capturing the user
behavioral pattern and the second component intends to calculate a threshold to be used during
the authentication phase for correlation purposes.
User behavior is defined as all kind of user interaction with his/her phone. That is, not limited to,
the applications he/she uses, the time he/she uses them, the duration of use, and the order of
In this research, we focus our study at analyzing the duration of use of each application. As an
example, figure 2 shows the duration of use of the WhatsApp application plotted against the start
time of each usage of that application during 5 full weekdays for two users. The data is taken raw
and not manipulated in any form. In our study, we assume that each user presents a unique
behavior in the duration of use of each application for the same time frame. We will attempt to
prove this hypothesis using real collected data and with the support of a mathematical model.
3.1.1!Learning Phase one
Instead of looking at the data at that large scale, we decided to reduce the time scale by looking
at individual hours over several days. That is, for example, examining the behavior of a user for
one application for one-hour stating at 6:00 PM over a month. Examining the data in this form
would create a much more consistent behavior than when looking at it as a whole. Before
modeling the captured data, we applied two preprocessing steps:
FIGURE 2: WhatsApp duration vs start time for user A and user B.
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 9
!!Data filtering step
First, the time stamp is divided into hour, minute and seconds. Then a new calculated number is
created to convert the minutes and seconds into seconds to retrieve the start time in terms of
seconds within that hour (figure 3). Next, the data is filtered by application (WhatsApp in our
example as depicted in figure 4).
FIGURE 3: Step 1: Time conversion.
FIGURE 4: Step 2: Application filtering
Next, the duration is filtered to values between 5 and 180 seconds in order to remove the
readings that were not meaningful in our approach; this is depicted in figure 5.
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 10
FIGURE 5: Step 3: Filtering for duration between 5 and 180 seconds
The data is now ready for modelling, as an example, we will take the hour 18:00 (this is depicted
in figure 6). The collected data set consists now of the converted start time in seconds, and
duration of use in seconds. The columns start time, application name, end time are no longer
needed. That data set is ordered in ascending converted start time (first column).
FIGURE 6: Result of the filtering
The "sec converted" column can be considered as the abscissa, and the "Duration" its ordinate.
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 11
To model this data set and avoid fluctuation and negative values in the interpolation, data is
sampled at a rate of 8.33 x 10-3 Hz, that is a reading every 2 minutes. Since the user does not
necessary use any application at that particular rate, the data is distributed to 31 points by
assigning it to the higher start time. The example is shown in figure 7 and in figure 8 The first
point is (0, 0). The 2nd start time 64 is less than 120, therefore, the duration 48 is assigned to
120. As for the start time 275, 287, 340, they all fall below 360, so an average of their duration is
taken and assigned to 360. As a result: Sometimes, the data acquired does not fill the 31 points
that represent an hour. As a solution, midpoints are used to bridge gaps. Using this method, the
same sample data used earlier is filtered, and the result is a curve showing one application
(WhatsApp), one user, and one hour over 5 consecutive weekdays figure 10. We can notice that
this time, the data is less and can be modelled. We will be looking next at a way to quantify that
behavior using a mathematical function.
14 No Author Given
Fig. 7. Result of the ﬁltering
so an average of their duration is taken and assigned to 360. As a result:
Sometimes, the data acquired does not ﬁll the 31 points that represent an
hour. As a solution, midpoints are used to bridge gaps. Using this method,
the same sample data used earlier is ﬁltered, and the result is a curve show-
ing one application (Whatsapp), one user, and one hour over 5 consecutive
weekdays (ﬁgure 9). We can notice that this time, the data is less and can be
modelled. We will be looking next at a way to quantify that behavior using
a mathematical function.
Tabl e 1 . Original data
Original start time Duration of use
FIGURE 7: Original data
Tabl e 2 . Original data reallocated
Reading every 2 minutes Allocated duration
Fig. 8. Whatsapp usage over 1 hour over 5 days
4 Cubic spline interpolation
The data collected from the user activity application is ﬁltered and ready for
modelling. Since we have a set of tuples (x , y), a polynomial function is needed.
As a ﬁrst test, on one time slot, one application was chosen. If this data were
modeled using high degree polynomial, the result would be as shown in ﬁgure
10, the curve would jump to high results at undesired locations. Also, the curve
does not respect the points given to it and is far from being accurate. The plot
below was conducted using a 9th degree polynomial using Matlab .
Given the low accuracy rate with a regular polynomial, we needed a function
that would reﬂect the actual user behavior without compromising its integrity.
Using the cubic spline polynomial leads us to our exact goal by modelling the
dataset without an error threshold. The reason for that is that this function is
based on individual cubic polynomials that link each 2 points in order to create
a smooth curve that passes through all the points. In the plot in ﬁgure 11, we
can see the same set of points modelled using the cubic spline function.
FIGURE 8: original data reallocated
FIGURE 9: WhatsApp 1hour 5 days
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 12
220.127.116.11!Cubic spline interpolation
The data collected is preprocessed and ready for modelling. Since we have a set of tuples (x , y),
a polynomial function is needed. As a first test, on one-time slot, one application was chosen. If
this data were modeled using high degree polynomial, the result would be as shown in figure 10,
the curve would jump to high results at undesired locations. Also, the curve does not respect the
points given to it and is far from being accurate. The plot below was conducted using a 9th
FIGURE 10: Data modelled using 9th degree polynomial
Given the low accuracy rate with a regular polynomial, we needed a function that would reflect
the actual user behavior without compromising its integrity. Using the cubic spline polynomial
leads us to our exact goal by modelling the dataset without an error threshold. The reason for that
is that this function is based on individual cubic polynomials that link each 2 points in order to
create a smooth curve that passes through all the points. In the plot in figure 11, we can see the
same set of points modelled using the cubic spline function.
FIGURE 11: Data modelled using cubic spline
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 13
18.104.22.168!Modelling data set
The cubic spline interpolation is used to model the dataset as shown in the first graph in figure 12.
The graphs show the duration of usage of the same application in seconds, for the same hour,
the same dates taken for 4 different users against the 31 points that represent the start time in
seconds. From the shape of the function, we can start to notice the de-correlation between users.
FIGURE 12: Data for four users interpolated using cubic spline polynomial
This function will be later used to authenticate the user from the duration of use of an application
and from the pattern of use that the function has learned throughout this learning phase. At run
time, the phone can send to the function the start time of an application and it will return the
expected duration of use. Comparing the obtained value with the original run-time value can
provide information about the authenticity of the user.
3.1.2! Learning Phase 2 (Threshold calculation)
The learning phase is made up of two phases. During the first phase a cubic spline function (for
each user) has been calculated based on data (user behavioral pattern) collected over one
month. The aim of the second learning phase is to calculate how much the user behavior
fluctuates over an average (called threshold). Below this threshold a user is considered as
genuine and above it is considered as an intruder. During this phase, the real captured (x, y) that
are collected over fifteen days are clustered by hours as explained in the first learning phase.
Each x (of each hour) is feed into the cubic spline function (generated during the first phase) to
calculate y’. Then, the absolute value of the difference between the calculated duration (y’) and
the real duration (y) is recorded. Finally, for every hour, the differences (|y – y’|) are averaged. As
depicts figure 13 the average is 24 during the hour 6 is 44 during the hour 7 (the unit of y, y’,
threshold and x is second). This would give us 24 averages(thresholds) for each user. This is
depicted in figure 12 where 24 averages for two users are plotted. Figure 14 depicts also that the
average varies form one user to another. The hour averages are considered as thresholds and
will be used at run time to authenticate a user. The threshold is considered as a maximum
deviation of the user from its normal behavior.
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 14
Difference Hour Minute Absice Threshold
YY' |Y;Y'| X AVERAGE
30 34 3 6 1 60
53 745 6 4 240
8 0 8 6 5 300
21 16 5 6 6 360
85 77 8 6 7 420
12 51 38 6 8 480
8 0 8 6 19 1140
14 7 7 6 20 1200
93 093 631 1860
19 019 634 2040
62 062 634 2040
6 0 6 6 35 2100
23 13 10 647 2820
23 617 652 3120
5 2 2 6 53 3180
69 126 57 654 3240
23 023 7 8 480 24
119 31 88 711 660
65 80 14 731 1860
51 051 735 2100
69 15 54 736 2160
93 14 79 737 2220
15 17 2 7 39 2340
13 70 57 8 1 60 44
25 30 5 8 6 360
FIGURE 13: Threshold (average) calculation for user 1
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
7 0 44
8 9 33
10 25 30
11 23 41
12 36 35
14 31 28
15 33 27
16 33 45
17 23 43
18 44 41
19 36 29
20 26 39
21 38 91
22 52 41
23 30 0
24 0 0
FIGURE 14: Thresholds (averages) for two users over 24 hours
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 15
3.2! The authentication component
The authentication component is based on the following algorithm: when new real (x, y) captured
(when a user finishes using an application), the start time of the activity (the abscise x) is
provided to the cubic spline function (generated during learning phase one) to calculate y’ and the
absolute value of the difference between the calculated and the real duration (|y – y’|) is
compared to the averaged difference (threshold) of the current hour generated during learning
phase two. If (|y – y’|) is less than the threshold, a zero (0) is recorded otherwise a one (1) is
recoded. Zero means he/she is possibly the owner and one means he/she is possible an
adversary. This is depicted in figure 16 in the column “below threshold”. The real authentication
takes place after five consecutive thresholds comparison (figure 15 & 16). The user is considered
as owner if the sum of the ones (1) is less or equal to two (2) otherwise he/she is considered as
an adversary. Under the column “Authentication of fire 15 we can notice that there are two results
less or equal to two (2), this means that the user is authenticated as the owner of the device.
Difference Hour Minute Absice
yy' |y>y'| X
5 0 5 5 29 1740 0 1
43 43 0 6 1 60 24 0
19 16 3 6 3 180 24 0
26 104 78 6 9 540 24 1
35 18 17 6 9 540 24 0 2
14 47 32 618 1080 24 1 2
67 28 39 618 1080 24 1 3
11 124 112 638 2280 24 1 4
FIGURE 15: Owner authentication
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 16
FIGURE 16: Authentication Algorithm
The MOUBE has been implemented within the middle ware SCAMMP   on Android
platform. The main goal of SCAMMP is to provide a middleware framework that offers high-level
context-aware information through a simple API to the application layer. To implement MOUBE
on SCAMMP, only one agent is needed. This agent encapsulates a software sensor namely, the
user behavioral sensor. It is defined as a kind of user interaction with his/her phone. The agent
records the start time and the duration in seconds.
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 17
In order to conduct this experiment, we restrict our study to smart phones with android platform.
The study consists of collecting user centric data to capture the user behavior. That data is
collected from 30 users over a sequence of 60 days. The learning phases run over forty-five (45)
days and the authentication period was limited to fifteen (15) days. The users were asked to run
the application on their phone and not to stop it till the end of the experiment. No special
behavioral requirement was asked of them.
After finishing the learning phases, the authentication phase was started to evaluate the MOUBE
First, the following experiments has been executed:
Sixteen (16) users were allowed to run the authentication phase using their cubic spline function
and thresholds (that were calculated during the learning phases 1&2) and it is expected to
positively authenticate the user as device owner. We except to collected the following values:
o!True positive: The user is indeed the owner and
o!False negative: The user is the owner of the device but the result suggests he is
Second, as for the remaining fourteen (14) users, we switched their cubic spline function and
thresholds. The group of these users should not be authenticated by the MOUBE system
because they are adversary.
We except to collected the following values:
o!True negative: The user is indeed an adversary.
o!False positive: The user is an adversary but the result suggests he is the owner.
The results of the above experiment are depicted in figure 17 below. We can notice that we were
able to achieve a positive identification of the owner 76%, and the intruder 64% on average.
These results are not as high for intruder detection and this is because of the high values of the
thresholds for some hours. But considering that this research work traces only one user behavior
it has an important achievement. Combining other user behaviors together will for sure provide
better identification rates.
True positive 76%
False negative 24%
True negative 64%
False negative 36%
FIGURE 17: Results average
5.!Conclusion and Future
We introduce a novel authentication model to be used as complementary to the existing models;
Particularly, the context of the user, the duration of usage of each application and the occurrence
time were examined and modeled using cubic spline function as authentication technique. A
software system composed of two software components has been implemented on Android
platform preliminary results shows a 76% accuracy rate in determining the rightful owner of the
The user behavior can be further expanded to cover things other than the application usage.
Everything that is affected by the user can be regarded as user behavior, for instance, the speed
of battery drain, the CPU percentage usage, data stream over the Wi-Fi and the mobile data
International Journal of Computer Science and Security (IJCSS), Volume (10) : Issue (3) : 2016 18
In this research we have considered every application to model a single user behavior, what still
can be explored is putting all collected user behaviors in a single matrix. Further, the matrix
eigenvalues can be used as a unique signature. One can use Fourier transformation to model the
user behavior over long period. In this work, we identify and model the user behavior to be used
as implicit authentication, nevertheless, we do believe that there is still a lot to explore in this field.
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