DETECTING ATTACKS ON MOBILE PLATFORMS USING ARTIFICIAL INTELLIGENCE: A CYBER BROTHER APPROACH

Authors

  • Abbaz Primbetov Senior Lecturer, Computer Engineering Department, Tashkent University of Applied Sciences Author
  • Xusniddin Xamidov, Kamron Mamedov, Muhammadillo Xakimov Students of Information Security, Tashkent University of Applied Sciences Author

DOI:

https://doi.org/10.65164/cz438417

Keywords:

Artificial Intelligence, Mobile Security, Cybersecurity, APK Analysis, URL Detection, Malware, On-device AI, Cyber Brother

Abstract

The rapid expansion of mobile ecosystems has significantly increased the attack surface for cyber threats. This paper explores the application of Artificial Intelligence (AI) techniques in detecting and mitigating cyberattacks on mobile platforms. As a case study, the Cyber Brother mobile security system is analyzed, focusing on its ability to detect malicious APK files and phishing URLs in real time. The system utilizes on-device machine learning models to ensure privacy-preserving and offline threat detection. Experimental observations indicate that AI-driven approaches outperform traditional signature-based methods, especially in identifying zero-day attacks. The study highlights the effectiveness, limitations, and future potential of AI-based mobile cybersecurity solutions.

References

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Published

2026-05-15

Issue

Vol. 3 No. 1 (2026): “GENERATIV SUN’IY INTELLEKT SHAROITIDA TA’LIM TIZIMLARI VA KIBERXAVFSIZLIKNI TRANSFORMATSIYA QILISH, TAHDIDLAR, ISHONCHLILIK MUAMMOLARI VA XAVFSIZ AI ARXITEKTURALARI” mavzusidagi xalqaro ilmiy-amaliy konferensiya to`plami (TAFU, 12-13-may)

Section

Articles