Evaluation of Avast Mobile Security

This report evaluates the usability and functionality of Avast mobile security, a popular mobile security application. It discusses the features of the application, such as malware detection, anti-theft locking, privacy advisor, Wi-Fi security, and call blocker. The report also includes a literature review and research section, along with conclusions drawn from the findings.

Mobile application is Avast mobile security
ABSTRACT
This report is associated with the evaluation of
Avast mobile security. Avast mobile application
provide security along with recognizing the
malwares in mobile devices, the mobile application
provide other functionality such as anti-theft
locking, privacy advisor, Wi-Fi security and call
blocker. In this paper, usability and functionality of
the Avast mobile security is evaluated along with a
literature review and research section. Additionally,
a conclusion has been drawn from the findings.
1. INTRODUCTION
Mobile phones are becoming a daily drive for most
of the people as it provides numerous
functionalities along with optimal usability. The
potential users of smartphones are also growing
rapidly, the number of smartphone user has grown
to 2.5 billion which is 36% of the world population.
While using the smartphones, users deals with huge
amount of data every day and some of them are
very crucial which needs to be keep safe from third
parties. Most of the mobile devices are also
frequently connected to the internet in order to
exchange data from servers or other devices. In
order to protect the mobile devices from any
vulnerability, mobile security applications are
necessary. Avast is one of the most recognized
company which provides security solutions for
several type of devices including mobile. The
Avast mobile application has more than 100
million downloads along with 4.5 user rating in
Google play. They tested their application against
6,300 samples of recent malware and the
application were able to detect 99% of them which
exceeds the industry average rate. Additionally,
Avast is designed to provide optimal usability to
the users as well. For instance, the application does
not consume much battery life which could result
in device slowdown or generate traffic [1]. Over
2,400 legitimate files were inserted into the device
to check for false positives, but Avast recognized
each one and did not create any false alarms [2].
Along with recognizing the malwares in mobile
devices, the mobile application provide other
functionality such as anti-theft locking, privacy
advisor, Wi-Fi security and call blocker.
2. USABILITY
In last couple of years, the Avast antivirus has gone
through some major changes as they started
providing free advertise support while
compromising the malware detection range. This
version is also believed to be impact the system
performance as well. However, later they fix things
up through the return of premium model which
provide constant support [3]. However, some
functionalities of the mobile application does not
work properly as the call blocker does not work
properly and the anti-theft features, while powerful,
had trouble connecting to the Avast web portal.
The application is free to download and use with
add-ons. However, they also offer premium
services without advertisement which costs 7.99
dollar per year [4]. The premium plan includes app
locking, removal of ads from the application.
3. FUNCTIONALITY
Most of the IOT devices available in the market
does not comes within built security which
increases the chance of risks. So, external
applications like Avast security is essential in order
to keep the devices safe from unwanted threats.
Avast constantly investing new technology in
association of the cloud based security
infrastructure which includes processing and
analysing IOT threat at the network level[5]. For
instance, the inspector technology embedded with
the Avast security for the Wi-fi connectivity assist
to identify vulnerability and remedy configuration
errors as well. Internally, Avast analyse the
behaviour of different IOT devices in order to
evaluate the type and amount of data used in the
conjunction. Simply, they study the type of
network users in and identify the vulnerability and
capability of such environment while utilizing
Artificial Intelligence engine [6]. Such approach
assist to keep the device safe from any unwanted
threat.
Avast frequently update their infrastructure while
running machine learning algorithms in their
distributed cloud in order to detect new threats on
mobile devices. Their optimal malware protection
provides capability to minimize latency which is
very useful to lessen the time to identify threats in
any mobile devices. However, providing support
for numerous type of mobile devices is a very
critical task. Several mobile manufacturing
company uses different type of infrastructure to
build their devices. In order to provide support for
different devices, Avast mainly utilizes two type of
approach. Firstly, they utilizes their customize
anomaly threat detection algorithm to identify
specific type of threats and secondly they utilizes
the learning over deep neural network to identify
different kind of attacks including the emerging
ones. Their customize network traffic feeds statics

from millions of connections into a deep neural
network. Then the interior nodes of complex
networks start analyze the flow according to the
specific type of devices. This type of analysis
generally associated with number of devices used
to in a home network, how a larger number of
devices make up a service[7]. Finally, the output or
final layer of the neural network separates out
collections of flows that are either benign, or
malicious.
Figure 1: Interface of Avast Application
In March of this year, the independent IT
security institute AV-TEST released a usability test
result of different security products including avast
security. The results was in favour of avast as it
was documented as most effective application
among others. Additionally, it was specified as the
best security application for android operating
system.
The protection score of the analysis shows
that, over 6300 malwares are tested against the
avast application, surprisingly, the application was
able to detect 99% of the real world malware.
Avast was rated 6 out of 6 which exceeds industry
average and earnings [8].
Over 2,400 legitimate files were inserted
into the device to check for false positives, but
Avast recognized each one and did not create any
false alarms. Along with recognizing the malwares
in mobile devices, the mobile application provide
other functionality such as anti-theft locking,
privacy advisor, Wi-Fi security and call blocker.
These usability test also earned Avast another
perfect 6 out of 6.
Figure 2: AV-TEST Report
4. LITERATURE REVIEW
Previous study has find that mobile devices are
affected by mainly two primary attack victors.
Firstly, when the devices is connects the internet
and the second when the devices connects to a
network. As huge amount of crucial financial and
personal data is stored in the mobile devices. This
is the main reasons hackers are attracted to the
phone environment. In last couple of years, 55,000
new mobile malwares ware founded which inclined
toward stealing private data and money [9]. The
android botnets are one of the emerging threats to a
traveling salesman with tuberculosis, due to the
itinerant nature of smartphone according to the
Marko a referenced author. Symantec investigator
Mario Ballano stated he has found a new attack
vector on Android, similar to Windows DLL
hijacking [10]. Mainly this type of threats are
associated with rebooting the device, generating
and sending a list of files contained on the device
blocking the device and displaying a web page
specified by the malicious server, Installing and
removing applications, downloading and running
files, sending, stealing, and deleting SMS,
recording, changing the C2 serve, making calls,
stealing contacts, checking balances, redirecting,
and blocking calls[11].
Even though, experts were able to identify
numerous type of threats, no optimal solution is
presented in this field. All cryptology (or security
protection of information) is based on two factors
something you know and something you own. An
access card and a password are perfect depictions
of this scenario [12]. It is observed that most of the
user does not enable the protection for a long
amount of time as it consumes very much energy.
If malware protection was more energy aware and
used less battery, it is more likely users would
leave these protection services on.

5. RESEARCH
The research was performed by overall 30 people
from different IT background. Users were asked to
use the Avast mobile security for couple of weeks
and submit their responses through the survey. The
main objectives of the research is to identify the
satisfaction level of the user along with the
protection level. They were asked to frequently
scan for malwares and noted down the number of
malware identified by the application throughout a
limited time. Most of the users do not use any kind
of mobile security generally. They were asked to
respond the difference experience while utilizing
no protection and using the Avast antivirus. The
users were asked to focus on the effectiveness of
the application along with the usability.
Number of
evaluators
reporting
issue
Low
impact
Medium
Impact
High
impact
1 7 12 7
2 5 2 1
4 3 4 1
Table 1: Issue detection frequency and impact
Heuristic Low
impact
Medium
Impact
High
impact
Power
consumptions
1 2 6
Identify
vulnerability
3 1 2
Virus
Protection
4 5 9
Standards and
consistency
2 1 1
Help users
recognise,
diagnose and
recover from
virus.
2 3 4
Flexibility and
efficiency of
use
3 4 6
Storage status 3 2 1
Table 2: Number of heuristic violations by
impact
6. CONCLUSION
It can be concluded that from above findings that
the Avast security application can be considered as
a daily driver for mobile devices. The common
scan results shows three vulnerability as unknown
sources enabled, advanced web shield off and SMS
shield disabled. The application were able to
identify the most common settings vulnerability.
The application was able to resolve the issue with
just couple of clicks. However, most of the users
were unsatisfied by the battery usages by the
application. After analysing the research result it
can be concluded that the avast mobile application
is very useful and provide an optimal value for
protecting the devices. The Avast mobile
application has more than 100 million downloads
along with 4.5 user rating in Google play. They
tested their application against 6,300 samples of
recent malware and the application were able to
detect 99% of them which exceeds the industry
average rate. Additionally, Avast is designed to
provide optimal usability to the users as well. For
instance, the application does not consume much
battery life which could result in device slowdown
or generate traffic.

7. REFERENCES:
[1.] Bhattacharya, P., Yang, L., Guo, M., Qian,
K. and Yang, M., 2014. Learning mobile
security with Labware. IEEE Security &
Privacy, 12(1), pp.69-72.
[2.] Ekberg, J.E., Kostiainen, K. and Asokan,
N., 2013, November. Trusted execution
environments on mobile devices. In
Proceedings of the 2013 ACM SIGSAC
conference on Computer &
communications security (pp. 1497-1498).
ACM.
[3.] Gelenbe, E., Görbil, G., Tzovaras, D.,
Liebergeld, S., Garcia, D., Baltatu, M. and
Lyberopoulos, G., 2013. NEMESYS:
Enhanced network security for seamless
service provisioning in the smart mobile
ecosystem. In Information Sciences and
Systems 2013 (pp. 369-378). Springer,
Cham.
[4.] Guo, M., Bhattacharya, P., Yang, M.,
Qian, K. and Yang, L., 2013, March.
Learning mobile security with android
security labware. In Proceeding of the
44th ACM technical symposium on
Computer science education (pp. 675-
680). ACM.
[5.] Hurel, G., Badonnel, R., Lahmadi, A. and
Festor, O., 2014, June. Outsourcing
mobile security in the cloud. In IFIP
International Conference on Autonomous
Infrastructure, Management and Security
(pp. 69-73). Springer, Berlin, Heidelberg.
[6.] Kravets, A.G., Bui, N.D. and Al-Ashval,
M., 2014, September. Mobile security
solution for enterprise network. In Joint
Conference on Knowledge-Based
Software Engineering (pp. 371-382).
Springer, Cham.
[7.] La Polla, M., Martinelli, F. and Sgandurra,
D., 2013. A survey on security for mobile
devices. IEEE communications surveys &
tutorials, 15(1), pp.446-471.
[8.] Li, Q. and Clark, G., 2013. Mobile
security: a look ahead. IEEE Security &
Privacy, 11(1), pp.78-81.
[9.] Penning, N., Hoffman, M., Nikolai, J. and
Wang, Y., 2014, May. Mobile malware
security challeges and cloud-based
detection. In Collaboration Technologies
and Systems (CTS), 2014 International
Conference on (pp. 181-188). IEEE.
[10.] Popa, D., Cremene, M., Borda,
M. and Boudaoud, K., 2013, January. A
security framework for mobile cloud
applications. In Roedunet International
Conference (RoEduNet), 2013 11th (pp. 1-
4). IEEE.
[11.] Salman, R., Willms, I., Sakamoto,
T., Sato, T. and Yarovoy, A., 2013,
October. Environmental imaging with a
mobile UWB security robot for indoor
localisation and positioning applications.
In Radar Conference (EuRAD), 2013
European (pp. 331-334). IEEE.
[12.] Zhu, J., Wu, P., Wang, X. and
Zhang, J., 2013, January. Sensec: Mobile
security through passive sensing. In 2013
International Conference on Computing,
Networking and Communications (ICNC)
(pp. 1128-1133). IEEE.

Task 2:
Risk Assessment
Activity/ Area
of Concern
New Factors
of Risk
Actions for
Minimizing the
Risk
Current
Factors of
Risk
Persons at
Risk
Identified
Hazard
Non-workable
functions
Return
appropriate
data and
analyse the
search pattern
of the users for
enhancing the
usability.
Enabling
integration of
the usability
attributes and
customization
for the users.
Monitoring the
navigation of
the pages and
decreasing the
loading time for
growing the
response.
Avast Functions like
call block does
not work at al.
Power
consumption
The application
use huge
amount of
power as it runs
all the time.
Trying to make
the software
lighter,
Analyzing the
requirement of
the user and
aligning the
interface with
the requirement
Avast Consume huge
amount of
power.
I hereby authorize to verify all the information I have provided, to obtain and disclose all the personal information
that is regarding the person and perform all the user queries suited for the development of the interface. The
verification is performed by engaging a group of user and distributing the user interface created for the
development of the application.
Name:
Address
Date
___________________________
_____________________
Signature
Data gathering Form

Problem found (write a single
problem in the space)
Heurist
ic(s)
violated
Where it was violated
How was it found
Seve
rity
Rati
ng
Task Location
Navigating
the website
Scanning for
problems
Understandi
ng the
content
Systematically
searching for
problems
Searching Trying to force
errors
Entering
data
Following users
task
Navigating
the website
Scanning for
problems
Understandi
ng the
content
Systematically
searching for
problems
Searching Trying to force
errors
Entering
data
Following users
task
Navigating
the website
Scanning for
problems
Understandi
ng the
content
Systematically
searching for
problems
Searching Trying to force
errors
Entering
data
Following users
task
Instructions to Participants
1. Install the application
2. Register with username and login
3. View initial screen and navigate to the other pages
4. Search for virus
5. Analyse the loading time
6. Identify vulnerabilities
7. Check power consumptions
8. Analyse the effectiveness
9. Provide Feedback
Full Result

The Avast security application can be considered as a daily driver for mobile devices. The common scan results
shows three vulnerability as unknown sources enabled, advanced web shield off and SMS shield disabled. The
application were able to identify the most common settings vulnerability. The application was able to resolve the
issue with just couple of clicks. However, most of the users were unsatisfied by the battery usages by the
application. After analysing the research result it can be concluded that the Avast mobile application is very
useful and provide an optimal value for protecting the devices.