This case study presents a new model called Mobotder to detect possible security exploits in mobile applications like online meeting and gaming apps. The model analyzes app features like geolocation, permissions, API calls and system calls. It evaluated 2694 malware and 1000 benign apps, achieving 99.1% accuracy in classifying apps as malicious or benign. The model found 10% of meeting apps and 30% of gaming apps have high or medium risk of exploit. This shows Mobotder can efficiently identify potential security issues in mobile apps.

1. MOBILE DEVICE
SECURITY
Assoc. Prof. Ts. Dr. Madihah Mohd Saudi
Faculty of Science & Technology, USIM

2. CONTENTS
• Overview
• Definition
• Mobile Device Security Objectives
• Mobile Security Threats
• Rethinking Mobile Security
• Mobile Security Best Practices
• Mobile Security Strategy
• Smartphone Surveillance Features
• Antivirus in Gadget
• Mobile Device Security: A buyer’s guide

3. Overview
Forecast number of mobile devices worldwide from 2020 to 2025 (in billions)*
• In 2021> number of
mobile devices operating
worldwide stood at
almost 15 billion.
• Expected to reach 18.22
billion by 2025

4. Why is mobile security important?
• The future of computers and communication lies
with mobile devices, such as laptops, tablets and
smartphones with desktop-computer capabilities.
• Their size, operating systems, applications and
processing power make them ideal to use from
any place with an internet connection.
• Because mobile devices have become more
affordable and portable, organizations and users
have preferred to buy and use them over desktop
computers.
• And with ubiquitous wireless internet access, all
varieties of mobile devices are becoming more
vulnerable to attacks and data breaches.

5. Definition
• Mobile device security:
• being free from danger or risk of an asset
loss/data loss using mobile computers &
communication hardware.
• combination of strategies & tools that
secure mobile devices against security
threats.
• protection of sensitive information
transmitted by your smartphones,
tablets, laptops, and other mobile
devices

6. Mobile device
security objectives Protect
sensitive
data stored
on portable
devices
Prevent
unauthorized
users from using
mobile devices
to access the
enterprise
network

7. Mobile Security Threats
Data
Leakage
Unsecured
Wi-Fi
Network
Spoofing
Phishing
Attacks
Spyware Broken
Cryptography
Improper
Session
Handling

8. Rethinking Mobile
Security
Manage the
devices
Protect the devices
Control the data

9. Mobile Security Best Practices
Utilize encryption
Enable remote data
wipe
Make user authentication
the highest priority
Update mobile operating
systems & on-board
applications with security
patches
Back up user data on a
regular basis

10. Be sure not to grant
unnecessary permissions
to applications
Install mobile security &
antivirus applications
Disable Bluetooth & Wi-
Fi when not needed
Be aware of social
engineering techniques
Be sure not to
jailbreak your device

11. Enable secure access
for mobile & remote
users
Segment your
network & begin
with a zero-trust
model
Identify devices
infected with
malware
Implement threat
prevention for
mobile devices
Enforce
device-based
security policy
Define exactly what is
permitted in your
mobile environment
Mobile
Security
Strategy

12. Global Positioning System
(GPS)
Short Message Service
(SMS)
Audio
Camera
Call Log
Smartphone
Surveillance
Features

13. Download antivirus
software from its
official
website/platform
Do not use free Wi-
Fi service in public
to reduce malware
attack risk
Choose reputable
antivirus software
Do not download
pirated antivirus
software
Install latest
antivirus
software on your
gadget
Ensure the OS on
gadget up to date
Be aware of latest
malware trends
Do not download
software from
unrecognized
source
1 8
7
2
6
3
5
4
Antivirus in Gadget:
Avoid Malware
Infection

14. Controlling
access to
your device
Keeping
your device
up to date
Using your
device’s
security &
privacy
features
Ensuring
your data
cannot be
accessed
Using
internet
securely
Reducing
the damage
of a lost or
stolen
device
Detecting &
preventing
malware
Mobile Device Security: A buyer’s guide

15. Controlling access to your device
What to look for
Devices that can
be unlocked in
different ways
Online accounts
that support
‘two-factor
authentication’
(2FA).
Devices that
reduce your
reliance on
passwords
Make sure that you
Set a screen
lock password,
PIN, or other
authentication
method (such as
fingerprint or
face unlock)
Secure any
linked online
accounts
Set up security
questions that
are hard to
guess
Follow the
manufacturer’s
guidance

16. Keeping your
device up to
date
What to look for
How often
are devices
updated by
the
manufacture
r?
How long
are devices
supported
by the
manufacture
r?
How often
are devices
upgraded by
your mobile
network?
How easy is
it to update
applications
?
Make sure that you
Check that
automatic
updates are
enabled on
your device.
Apply device
updates
within a few
days of
being
prompted
Keep your
apps up to
date.

17. Using your
device’s security
& privacy
features
What to look for
Devices running the latest
versions of the device
software
Read the manufacturer’s
guidance on how to use the
security features of your
device
Make sure that you
Keep your device up to date
Can trust any external
device you’re connecting
the device to
Don’t disable any of the
security features that come
with your device.

18. Detecting & preventing malware
What to look for
Devices
featuring a
built-in app
store
Devices
featuring a
built-in anti-
malware app
Devices that let
you prevent
apps from
accessing your
data.
Make sure that
you
Only get apps
from the
device’s app
store.
Review the
permissions that
apps ask for

19. Ensuring
your
data
cannot
be
accessed
What to look for
Devices that have
encryption enabled by
default
Devices that encrypt the
memory card
Devices that support
specialized hardware
encryption
Make sure that you
Turn on storage encryption
if it is not already enabled
by default.
Turn on encryption of the
device’s memory card
Securely erase the data on
your device before you sell
it.

20. Using the internet
securely
What to look for
Devices with an up-
to-date browser
Apps that make
public statements
about their network
security
Make sure that you
Look for the padlock
symbol when
making transactions
on the Internet
Take care using
public Wi-Fi
networks
Disable any services
and uninstall any
apps you don’t
intend to use.

21. Reducing the damage of a
lost or stolen device
What to look for
Manufacturers that include
an online service to locate
lost devices.
Devices that can
automatically backup your
data online
Make sure that you
Turn on the anti-theft
features on your device
before you have a chance
to lose it.
Turn on automatic backup
if it’s available.
Set a PIN on your SIM card

22. CASE STUDY
Securing Mobile Applications Against Mobile Malware Attacks:
A Case Study

23. SCOReD 2021 |19th IEEE Student Conference on Research and Development, 23-25 November 2021, Kota Kinabalu Malaysia
Introduction
• The increasing demand for online meetings and online learning
• led to different security challenges by the cyber attackers, such as
exploiting the unpatched or defaults setting for online applications.1,2
• In 2020 >
• in the Dark Web, there were more than 500,000 exploited Zoom accounts
credentials were discovered3
• the Valve game was among the victim of security exploitation7
• This paper presents a new model called Mobotder to detect possible
security exploitation for online meeting applications and online games
based on geolocation (GPS), permissions, Application Programming
Interface (API) calls, and system calls.
Securing Mobile Applications Against Mobile Malware Attacks: A Case Study (1570767839)

24. SCOReD 2021 |19th IEEE Student Conference on Research and Development, 23-25 November 2021, Kota Kinabalu Malaysia
Methods
Fig 1. Method summarization for
feature selection
Fig 2. Overall processes involved for
Mobotder development
Securing Mobile Applications Against Mobile Malware Attacks: A Case Study (1570767839)
Fig. 1 Fig. 2

25. SCOReD 2021 |19th IEEE Student Conference on Research and Development, 23-25 November 2021, Kota Kinabalu Malaysia
Methods
• Datasets
• 2694 malware datasets > Drebin
project's training
• 1000 anonymous dataset> Google
Play store for evaluation.
• Hybrid analysis was used to reverse
engineer all the datasets.
Securing Mobile Applications Against Mobile Malware Attacks: A Case Study (1570767839)
Fig. 3

26. SCOReD 2021 |19th IEEE Student Conference on Research and Development, 23-25 November 2021, Kota Kinabalu Malaysia
Findings
Securing Mobile Applications Against Mobile Malware Attacks: A Case Study (1570767839)
Fig.4: Example of Mobotder
Mobile App Simulation for Online
Gaming Mobile Apps Evaluation
Fig.5: Example of Mobotder
Mobile App Simulation for Online
Meeting Mobile Apps Evaluation
Feature Work by
[19]
Work by [20] Work by [21] Work by [22] Mobotder
Number of
Samples
(Malware/
Benign)
1929/
150
250/
250
5560/ 5560 1931/1150 2694/1000
Number of
Features
(Permission/AP
I calls)
63/
1414
12
/8
Not stated the
number of
Permission, API
calls, hardware
components,
intents
Not stated the
number of
Permission, API
calls, intents,
metadata, system
calls, network
30/38
ML Classifier
Accuracy
Rate(%)
Random
Forest
93.9
PSO-ANFIS
89
Random Forest
97.24
Random Forest
97.48
Random
Forest
99.1
Comparison with previous studies

27. SCOReD 2021 |19th IEEE Student Conference on Research and Development, 23-25 November 2021, Kota Kinabalu Malaysia
Findings
• Online Meeting Apps
• 10% High, 80% Medium & 10%
Low
• Online Games Apps:
• 70% Medium, 30% Low
• These results indicated a
possibility of security
exploitation by the mobile
malware against the online
mobile apps.
• With the Mobotder model, any
possible security exploitation
could be identified quickly and
efficiently.
Securing Mobile Applications Against Mobile Malware Attacks: A Case Study (1570767839)
0
10
20
30
40
50
60
70
80
Online Meeting Apps
Online Games Apps
10
0
80
70
10
30
Applications categories with risk percentage
High risk Medium risk Low risk

28. SCOReD 2021 |19th IEEE Student Conference on Research and Development, 23-25 November 2021, Kota Kinabalu Malaysia
Conclusion
• Every online app has its own risk related to security
exploitation.
• The developed model used the permissions and API calls as
the underlying concept and input for the Mobotder model
development.
• It is proven that security exploitation can be detected with a
suitable and right feature selection.
Securing Mobile Applications Against Mobile Malware Attacks: A Case Study (1570767839)

29. SCOReD 2021 |19th IEEE Student Conference on Research and Development, 23-25 November 2021, Kota Kinabalu Malaysia
References
[1] P. Laplante, “Contactless u: Higher education in the postcoronavirus world,” Computer (Long. Beach. Calif)., vol. 53, no. 7, pp. 76–79, Jul. 2020, doi:
10.1109/MC.2020.2990360.
[2] M. Humayun, M. Niazi, · Nz Jhanjhi, · Mohammad Alshayeb, and · Sajjad Mahmood, “Cyber Security Threats and Vulnerabilities: A Systematic Mapping
Study,” Arab. J. Sci. Eng., vol. 45, no. 3, pp. 3171–3189, 2020, doi: 10.1007/s13369-019-04319-2.
[3] Paul Wagenseil, “Zoom security issues: Everything that’s gone wrong (so far) ,” Tom’s Guide, 2020. https://www.tomsguide.com/news/zoom-security-
privacy-woes (accessed Mar. 23, 2021).
[7] Sue Poremba, “Online Gaming Adds More Risk to WFH - Security Boulevard,” Security Boulevard, Jan. 04, 2020.
https://securityboulevard.com/2021/01/online-gaming-adds-more-risk-to-wfh/ (accessed Mar. 24, 2021).
[19] L. Onwuzurike, E. Mariconti, P. Andriotis, E. De Cristofaro, G. Ross, and G. Stringhini, “Mamadroid: Detecting android malware by building Markov chains of
behavioral models (extended version),” ACM Trans. Priv. Secur., vol. 22, no. 2, 2019, doi: 10.1145/3313391.
[20] A. Feizollah, N. B. Anuar, R. Salleh, G. Suarez-Tangil, and S. Furnell, “AndroDialysis: Analysis of Android Intent Effectiveness in Malware Detection,” Comput.
Secur., vol. 65, pp. 121–134, Mar. 2017, doi: 10.1016/J.COSE.2016.11.007.
[21] E. M. B. Karbab, M. Debbabi, A. Derhab, and D. Mouheb, “MalDozer: Automatic framework for android malware detection using deep learning,” in
Proceedings of the 5fth Annual DFRWS Europe, Mar. 2018, vol. 24, pp. S48–S59, doi: 10.1016/J.DIIN.2018.01.007.
[22] C. Tansettanakorn, S. Thongprasit, S. Thamkongka, and V. Visoottiviseth, “ABIS: A prototype of Android Botnet Identification System,” Proc. 2016 5th ICT
Int. Student Proj. Conf. ICT-ISPC 2016, pp. 1–5, Jul. 2016, doi: 10.1109/ICT-ISPC.2016.7519221.
Securing Mobile Applications Against Mobile Malware Attacks: A Case Study (1570767839)