Received 09.10.2025, Revised 05.01.2026, Accepted 26.03.2026 Published 20.04.2026

Method for protection of unstructured information on modern mobile platforms: Threat modelling and effectiveness analysis

Evgen Brovchenko, Valeriy Samaraj

The study aimed to develop a comprehensive approach to protecting unstructured information on mobile platforms by combining cryptographic algorithms, multi-factor authentication, machine learning methods, and blockchain technologies to create an adaptive security system. The research methodology was based on a theoretical analysis of scientific sources and modelling of the architecture of a system for protecting unstructured information, focused on modern mobile platforms. The study addressed the use of devices with support for Advanced RISC Machine TrustZone and Secure Enclave, which provide hardware isolation of cryptographic operations. Advanced Encryption Standard was used as the basic encryption algorithm for symmetric data protection, and Learning with Errors was used as a quantum-resistant mechanism. As part of the research, a conceptual multi-level model of an integrated security system was developed, including four interacting layers: cryptographic, authentication, analytical (behavioural analytics and machine learning methods) and blockchain. Each layer performs a separate function: encryption and hardware isolation of operations, user authentication, anomaly detection, and data integrity assurance. Together, they form an adaptive security system for mobile platforms. Implementation of a hybrid blockchain, which combines the high performance of private chains with independent verification of transactions in public blocks, was emphasised. This approach ensured a balance between transparency, energy efficiency, and resistance to modifications. Theoretical analysis confirmed that integrating these components into a single architecture creates conditions for the formation of an adaptive security system capable of dynamically responding to threats and ensuring a high level of protection for unstructured data in mobile environments. The proposed approach can be implemented in medicine, finance, public administration, and other areas where the protection of unstructured information is critical

multi-factor authentication; recurrent neural networks; logistic regression; adaptive encryption; hybrid blockchain architecture
60-71
Brovchenko, E., & Samaraj, V. (2026). Method for protection of unstructured information on modern mobile platforms: Threat modelling and effectiveness analysis. Information Technologies and Computer Engineering, 23(1), 60-71. https://doi.org/10.31649/vitce/1.2026.60

References

  1. Abbott, J., & Patil, S. (2020). How mandatory second factor affects the authentication user experience. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1-13). New York: ACM. doi: 10.1145/3313831.3376457.
  2. Abuhamad, M., Abusnaina, A., Nyang, D., & Mohaisen, D. (2020). Sensor-based continuous authentication of smartphones’ users using behavioral biometrics: A contemporary survey. ArXiv. doi: 10.48550/arXiv.2001.08578.
  3. Aburbeian, A.M., & Fernández-Veiga, M. (2024). Secure internet financial transactions: A framework integrating multi-factor authentication and machine learning. AI, 5(1), 177-194. doi: 10.3390/ai5010010.
  4. Acien, A., Morales, A., Vera-Rodriguez, R., & Fierrez, J. (2020). Mobile active authentication based on multiple biometric and behavioral patterns. In T. Bourlai, P. Karampelas & V.M. Patel (Eds.), Securing social identity in mobile platforms: Technologies for security, privacy and identity management (pp. 161-177). Cham: Springer. doi: 10.1007/978-3-030-39489-9_9.
  5. Ackerson, J.M., Dave, R., & Seliya, N. (2021). Applications of recurrent neural network for biometric authentication & anomaly detection. Information, 12(7), article number 272. doi: 10.3390/info12070272.
  6. Alenezi, M.N., Alabdulrazzaq, H., Alhatlani, H.M., & Alobaid, F.A. (2024). Performance of AES algorithm variants. International Journal of Information and Computer Security, 23(3), 322-337. doi: 10.1504/IJICS.2024.138494.
  7. Anyam, J., Singh, R.R., Larijani, H., & Philip, A. (2025). Empirical performance analysis of WireGuard vs. OpenVPN in cloud and virtualised environments under simulated network conditions. Computers, 14(8), article number 326. doi: 10.3390/computers14080326.
  8. Arslan, B., Gunduz, S., & Sagiroglu, S. (2016). A review on mobile threats and ML-based detection approaches. In 2016 4th international symposium on digital forensic and security (pp. 7-13). Little Rock: IEEE. doi: 10.1109/ISDFS.2016.7473509.
  9. Asif, R. (2021). Post-quantum cryptosystems for internet-of-things: A survey on lattice-based algorithms. IoT, 2(1), 71-91. doi: 10.3390/iot2010005.
  10. Banoth, R., & Regar, R. (2023). An introduction to classical and modern cryptography. In Classical and modern cryptography for beginners (pp. 1-46). Cham: Springer. doi: 10.1007/978-3-031-32959-3_1.
  11. Bonnie, E. (2025). 110+ of the latest data breach statistics to know for 2026 & beyond. Retrieved from https://secureframe.com/blog/data-breach-statistics.
  12. Brovchenko, E.M., Samarai, V.P., Datsenko, I.P., Pavlenko, V.I., & Sereda, A.V. (2023). Protection of unstructured information on a mobile device. Infocommunication and Computer Technologies, 1(5), 194-200. doi: 10.36994/2788-5518-2023-01-05-21.
  13. Buriro, A., Gupta, S., Yautsiukhin, A., & Crispo, B. (2021). Risk-driven behavioral biometric-based one-shot-cum-continuous user authentication scheme. Journal of Signal Processing Systems, 93(9), 989-1006. doi: 10.1007/s11265-021-01654-2.
  14. Chen, X., Miraz, M.H., Gazi, A.I., Rahaman, A., Habib, M., & Hossain, A.I. (2022). Factors affecting cryptocurrency adoption in digital business transactions: The mediating role of customer satisfaction. Technology in Society, 70, article number 102059. doi: 10.1016/j.techsoc.2022.102059.
  15. Ciaburro, G., & Iannace, G. (2021). Machine learning-based algorithms to knowledge extraction from time series data: A review. Data, 6(6), article umber 55. doi: 10.3390/data6060055.
  16. Ellavarason, E., Guest, R., Deravi, F., Sanchez-Riello, R., & Corsetti, B. (2020). Touch-dynamics based behavioural biometrics on mobile devices – a review from a usability and performance perspective. ACM Computing Surveys, 53(6), article number 120. doi: 10.1145/3394713.
  17. Farissi, A., Pradata, A., & Miraswan, K. (2023). Securing messages using AES algorithm and blockchain technology on mobile devices. Synchronous, 7(2), 1166-1171. doi: 10.33395/sinkron.v8i2.12381.
  18. Ferrag, M.A., Maglaras, L., Derhab, A., & Janicke, H. (2020). Authentication schemes for smart mobile devices: Threat models, countermeasures, and open research issues. Telecommunication Systems, 73(2), 317-348. doi: 10.1007/s11235-019-00612-5.
  19. Google for Developers. (2025). Android Keystore system. Retrieved from https://developer.android.com/privacy-and-security/keystore.
  20. He, Y., Huang, D., Chen, L., Ni, Y., & Ma, X. (2022). A survey on Zero Trust architecture: Challenges and future trends. Wireless Communications and Mobile Computing, 2022(1), article number 6476274. doi: 10.1155/2022/6476274.
  21. iOS Security: iOS 12.3. (2019). Retrieved from https://css.csail.mit.edu/6.858/2023/readings/ios-security-may19.pdf.
  22. Ismail, S., Nouman, M., Dawoud, D.W., & Reza, H. (2024). Towards a lightweight security framework using blockchain and machine learning. Blockchain: Research and Applications, 5(1), article number 100174. doi: 10.1016/j.bcra.2023.100174.
  23. Jumani, F., & Raza, M. (2025). Machine learning for anomaly detection in blockchain: A critical analysis, empirical validation, and future outlook. Computers, 14(7), article number 247. doi: 10.3390/computers14070247.
  24. Kandula, S.R. (2025). Breaking traditional encryption: Quantum computing risks to web and mobile applications. International Journal of Advanced Research in Engineering and Technology, 16(2), 329-342. doi: 10.34218/IJARET_16_02_020.
  25. Karuppiah, S.P. (2025). Understanding the behaviour of business users in multi-factor authentication adoption. (Master’s thesis, Lappeenranta-Lahti University of Technology LUT, Lappeenranta, Finland).
  26. Khan, S., Krishnamoorthy, P., Goswami, M., Rakhimjionovna, F.M., Mohammed, S.A., & Menaga, D. (2024). Quantum computing and its implications for cybersecurity: A comprehensive review of emerging threats and defenses. Nanotechnology Perceptions, 20(13), 1232-1248. doi: 10.62441/nano-ntp.v20iS13.79.
  27. Kim, G.-Y., Lim, S.-M., & Euom, I.-C. (2022). A study on performance metrics for anomaly detection based on industrial control system operation data. Electronics, 11(8), article number 1213. doi: 10.3390/electronics11081213.
  28. Kokal, S., Vanamala, M., & Dave, R. (2023). Deep learning and machine learning, better together than apart: A review on biometrics mobile authentication. Journal of Cybersecurity and Privacy, 3(2), 227-258. doi: 10.3390/jcp3020013.
  29. Kumar, N. (2025). Latest smartphone usage statistics 2026 (Worldwide). Retrieved from https://www.demandsage.com/smartphone-usage-statistics.
  30. Lim, W.Y.B., Luong, N.C., Hoang, D.T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020). Federated learning in mobile edge networks: A comprehensive survey. ArXiv. doi: 10.48550/arXiv.1909.11875.
  31. Liu, Y., He, D., Obaidat, M.S., Kumar, N., Khan, M.K., & Choo, K.-K.R. (2020). Blockchain-based identity management systems: A review. Journal of Network and Computer Applications, 166, article number 102731. doi: 10.1016/j.jnca.2020.102731.
  32. Mahor, V., Pachlasiya, K., Garg, B., Chouhan, M., Telang, S., & Rawat, R. (2021). Mobile operating system (Android) vulnerability analysis using machine learning. In D. Giri, J.K. Mandal, K. Sakurai & D. De (Eds.), Proceedings of international conference on network security and blockchain technology: ICNSBT 2021 (pp. 159-169). Singapore: Springer. doi: 10.1007/978-981-19-3182-6_13.
  33. Malik, G. (2024). Biometric authentication: Risks and advancements in biometric security systems. Journal of Computer Science and Technology Studies, 6(3), 159-180. doi: 10.32996/jcsts.2024.6.3.14.
  34. Martín, G.A., Fernández-Isabel, A., de Diego, I.M., & Beltrán, M. (2021). A survey for user behavior analysis based on machine learning techniques: Current models and applications. Applied Intelligence, 51(8), 6029-6055. doi: 10.1007/s10489-020-02160-x.
  35. Mehwish, Zaheer, M., Azeem, M.H., Afzal, Z., & Karim, H. (2024). Critical evaluation of data privacy and security threats in federated learning: Issues and challenges related to privacy and security in IoT. Spectrum of Engineering Sciences, 2(5), 458-479.
  36. Prokopovych-Tkachenko, D., Bakuta, A., Zverev, V., Kozachenko, I., & Cherkasky, O. (2025). Modeling phishing scenarios in Ukraine cyberspace: An analytical approach using Grafana-board. Electronic Professional Scientific Journal “Cybersecurity: Education, Science, Technique”, 1(29), 331-347. doi: 10.28925/2663-4023.2025.29.881.
  37. Rezk, N.M., Purnaprajna, M., Nordström, T., & Ul-Abdin, Z. (2020). Recurrent neural networks: An embedded computing perspective. IEEE Access, 8, 57967-57996. doi: 10.1109/ACCESS.2020.2982416.
  38. Sarkar, S., Choudhary, G., Shandilya, S.K., Hussain, A., & Kim, H. (2022). Security of Zero Trust networks in cloud computing: A comparative review. Sustainability, 14(18), article number 11213. doi: 10.3390/su141811213.
  39. Seto, H., et al. (2022). Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data. Scientific Reports, 12, article number 15889. doi: 10.1038/s41598-022-20149-z.
  40. Shamsuddin, N.S.M., & Pitchay, S.A. (2020). Implementing location-based cryptography on mobile application design to secure data in cloud storage. Journal of Physics: Conference Series, 1551, article number 012008. doi: 10.1088/1742-6596/1551/1/012008.
  41. Shifa, A., Asghar, M.N., Fleury, M., Kanwal, N., Ansari, M.S., Lee, B., Herbst, M., & Qiao, Y. (2020). MULVIS: Multi-level encryption based security system for surveillance videos. IEEE Access, 8, 177131-177155. doi: 10.1109/ACCESS.2020.3024926.
  42. Smith, G. (2025). +95 cyber security breach statistics 2025. Retrieved from https://www.stationx.net/cyber-security-breach-statistics.
  43. Wei, X. (2022). Smart mobile information systems and blockchain privacy protection. Mathematical Problems in Engineering, 2022(1), article number 5126326. doi: 10.1155/2022/5126326.
  44. Woźniak, M., Siłka, J., Wieczorek, M., & Alrashoud, M. (2021). Recurrent neural network model for IoT malware detection. IEEE Transactions on Industrial Informatics, 17(8), 5583-5594. doi: 10.1109/TII.2020.3021689.
  45. Yadav, A.K. (2021). Significance of elliptic curve cryptography in blockchain IoT with comparative analysis of RSA algorithm. In 2021 international conference on computing, communication, and intelligent systems (pp. 256-262). Greater Noida: IEEE. doi: 10.1109/ICCCIS51004.2021.9397166.
  46. Zimba, A., Phiri, K.O., Mulenga, M., & Mukupa, G. (2025). A systematic literature review of blockchain technology and energy efficiency based on consensus mechanisms, architectural innovations, and sustainable solutions. Discover Analytics, 3(1), article number 14. doi: 10.1007/s44257-025-00041-6.