Development of forensic anti-fraud method

1.

ANTI-FRAUD AI
Team: Mentor –Kamshat Asmaganbetova
Student AITU and Senior Flutter developers : Sanzhar
Chagirov , Amir Tleuzhanov
Student AITU and researcher : Sanzhar Pernebayev

2.

Plan
№
Наименование задач и
мероприятий по их
реализации
месяцы
Ожидаемые результаты реализации проекта (в
разрезе задач и мероприятий), форма завершения
Создание софта и написание статьи в Scopus .
1
Сбор данных
Разработка софта
Тестирование софта
Написание статьи
Февраль
Март
Апрель
Май
Июнь
18.01.2023

3.

Analysis
National cybersecurity index
NCSI :: Kazakhstan (ega.ee)
18.01.2023

4.

Relevance
◦ The research focuses on the development of a machine learning algorithm model to
detect vulnerabilities in mobile applications using the banking payment system
◦ With the increasing popularity of mobile technology, the spread of mobile banking
applications has grown
◦ Mobile banking applications have access to confidential user data and must manage it
with a high degree of security
◦ The study examines the vulnerabilities of mobile banking applications and develops
an advanced method to detect them to ensure the security of confidential data
◦ Anti-fraud AI can be used to detect and mitigate server loads.
18.01.2023

5.

Result
◦ software that uses forensic methods and machine learning algorithms to identify mobile banking
vulnerabilities and combat fraud would be an increase in the detection and prevention of fraudulent
activity.
◦ identify mobile banking vulnerabilities would be the detection of a fraudulent transaction.
◦ software use machine learning algorithms to confirm the potential fraud by analyzing other data points
such as the IP address, device used and the user's past transaction history
◦ Science paper (Scopus)
18.01.2023

6.

Reference
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1. Python for cybersecurity using Python for cyber offense and defense, Howard E.Poston , Waley
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2. Islam, M.S.: Systematic literature review: security challenges of mobile banking and
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payments system. Int. J. u- e-Serv. Sci. Technol. 7(6), 107–116 (2014)
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3. Mueller, B., Scheier, S., Willemsen, J.: Mobile Security Testing Guide (MSTG). Open Web
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Application Security Project (OWASP), pp. 1–412 (2019)
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4. Osho, O., Ohida, S.O.: Comparative evaluation of mobile forensic tools. IJ Inf. Technol.
Comput. Sci. 1(January), 74–83 (2016)
5. Scheier, S., Willemsen, J.: OWASP Mobile Application Security Verification Standard
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(MASVS) version 1.1.3. Open Web Application Security Project (OWASP), 99. 1–32
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(2019)
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6. Chanajitt, R., Viriyasitavat, W., Choo, K.R.: Forensic analysis and security assessment of
Android m-banking apps. Aust. J. Forensic Sci. 50(1), 3–19 (2018)
7. Al Mutawa, N., Baggili, I., Marrington, A.: Forensic analysis of social networking
applications on mobile devices. Digit. Invest 9(Suppl), S24–S33 (2012)
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18.01.2023