Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Theft of
personal identity is an unlawful act, a criminal in this case possesses or
attempts to be in possession of an identity of a victim without their knowledge
nor consent. Mobile identity theft the problem that inspires this study is just
one of the types of identity theft and refers to having control of a mobile
subscriber identification through SIM card registration and replacement
services again without the authority of the sole owner. This study provides a
solution to solve a gap that is addressed by empirical studies from both
academia and the industry for a problem that researchers feel should be a none
issue in the twenty first century. The study besides interprets the course of
mobile identity theft problem going by
the literature reviewed to be orchestrated by criminals who leverage
vulnerabilities at Subscriber Identity Module registration and replacement
processes. The study then proposes, develops and tests an integrated
authentication scheme based on existing models and inspired by theory of human
identification to hypothesize that addition of Integrated population
registration records would mitigate the problem.
The simulation process
of the proposed model is guided by an algorithm that employs a formula which
determines strength of authentication score, using data generated by constructs
of the scheme various results provide clarification on the safety of the model
when various parameters are changed. The study observer’s a maximum
authentication score at 96.43% when level of security is highest for all
parameters in the new authentication model against that of 95.37% when security
levels of the current authentication model are highest. The study hereby
confirms that highest level of authentication can be achieved by introducing an
integrated population records to the already existing authentication model
while their levels of security are maximum.
Country : Kenya
IRJIET, Volume 6, Issue 1, January 2022 pp. 68-76
1. Wyre, M., et al., The identity theft response system.
2020(592): p. 1-18.
2. Wyre,
M., D. Lacey, and K.J.C.R.G. Allan, Australia’s
Identity Theft Response System: Addressing the Needs of Victims. 2020.
3. Otor,
S.U., et al., An Improved Security Model
for Nigerian Unstructured Supplementary Services Data Mobile BankingPlatform.
2020.
4. Wangui,
M., D.J.I.R.J.o.B. Nzuki, and S. Management, THE EFFECT OF ELECTRONIC MONEY TRANSFER SYSTEMS ON THE FINANCIAL
PERFORMANCE OF FINANCIAL INSTITUTIONS IN KENYA (CASE STUDY OF SUMAC DEPOSIT
TAKING MICROFINANCE LTD). 2021. 2(1).
5. Roussos,
G., D. Peterson, and U.J.I.J.o.E.C. Patel, Mobile
identity management: An enacted view. 2003. 8(1): p. 81-100.
6. LoPucki,
L.M.J.T.L.R., Human identification theory
and the identity theft problem. 2001. 80:
p. 89.
7. Mayrhofer,
R., V. Mohan, and S.J.a.p.a. Sigg, Adversary
Models for Mobile Device Authentication. 2020.
8. Funcion,
D.G.J.J.o.S., Engineering and Technology, Content
Analysis of Online Documents on Identity Theft Using Latent Dirichlet
Allocation Algorithm. 2017. 5:
p. 56-68.
9. Jiang,
C. and Z. Li, Mobile Payment
Authentication, in Mobile Information
Service for Networks. 2020, Springer. p. 207-242.
10. Feng,
T., et al. Continuous mobile
authentication using touchscreen gestures. in 2012 IEEE conference on technologies for homeland security (HST).
2012. IEEE.
11. Cui,
Z., et al., A hybrid BlockChain-based
identity authentication scheme for multi-WSN. 2020. 13(2): p. 241-251.
12.Chen,
S., et al., Radio frequency
fingerprint-based intelligent mobile edge computing for internet of things
authentication. 2019. 19(16): p.
3610.
13. Kim,
J., N.J.P. Park, and U. Computing, Lightweight
knowledge-based authentication model for intelligent closed circuit television
in mobile personal computing. 2019: p. 1-9.
14. Hammood,
W.A., et al. A review of user authentication
model for online banking system based on mobile IMEI number. in IOP Conference Series: Materials Science and
Engineering. 2020. IOP Publishing.
15. Amine
Ferrag, M., et al., Authentication
schemes for Smart Mobile Devices: Threat Models, Countermeasures, and Open
Research Issues. 2018: p. arXiv: 1803.10281.
16. Chen,
D., et al., A behavioral authentication
method for mobile based on browsing behaviors. 2019. 7(6): p. 1528-1541.
17. Ma,
P., D. Tao, and T. Wu. A pseudonym based
anonymous identity authentication mechanism for mobile crowd sensing. in 2017 3rd International Conference on Big
Data Computing and Communications (BIGCOM). 2017. IEEE.
18. Li,
Y., et al., A Secure Anonymous
Identity-Based Scheme in New Authentication Architecture for Mobile Edge Computing.
2020.
19. Xue,
K., et al., A lightweight dynamic
pseudonym identity based authentication and key agreement protocol without
verification tables for multi-server architecture. 2014. 80(1): p. 195-206.
20. Ashibani,
Y. and Q.H. Mahmoud. A user authentication
model for IoT networks based on app traffic patterns. in 2018 IEEE 9th Annual Information Technology,
Electronics and Mobile Communication Conference (IEMCON). 2018. IEEE.
21. Mufandaidza,
M., T. Ramotsoela, and G.P. Hancke. Continuous
user authentication in smartphones using gait analysis. in IECON 2018-44th Annual Conference of the
IEEE Industrial electronics society. 2018. IEEE.
22. Xu,
J., et al., An identity management and
authentication scheme based on redactable blockchain for mobile networks.
2020. 69(6): p. 6688-6698.
23. Ali,
G., M. Ally Dida, and A.J.F.I. Elikana Sam, Two-Factor
Authentication Scheme for Mobile Money: A Review of Threat Models and
Countermeasures. 2020. 12(10):
p. 160.
24. Chege,
A.M., Implementation of the national
population registry system in Kenya. 2015, University of Nairobi.
25. Information
Technology Laboratory, N., Measuring
Strength of Authentication, in Advanced
Identity Workshop. 2015, National Institute of Standards and Technology
(NIST): Gaithersburg, Maryland. p. 9.