DETECTING BOTS IN WEB APPLICATIONS BASED ON USER BEHAVIOR USING MACHINE LEARNING

Zaynalov , Nodir, Maxmadiyorov, Faxriddin

Techscience.uz - техника фанлари долзарб масалалри · 2025-yil

Annotatsiya

This article proposes an innovative machine learning (ML)-based approach to distinguish between users and bots in web applications. Due to the declining effectiveness and user inconvenience of traditional methods (e.g., CAPTCHA), the authors suggest detecting bots by analyzing interactive user behavior, such as mouse movements, keyboard inputs, scrolling patterns, and other parameters. The study employs a lightweight neural network model in ONNX format, demonstrating high accuracy (93.5%) and low error rates in real-time bot detection. Key advantages of the model include minimal impact on user experience, fast processing (<50ms), and adaptability to various devices

Maqola ma’lumotlari
MualliflarZaynalov , Nodir, Maxmadiyorov, Faxriddin
JurnalTechscience.uz - техника фанлари долзарб масалалри
Nashr sanasi2025-08-11
Jild3
Son5
Betlar11-16
TilO‘zbek
DOI10.47390/ts-v3i5y2025n2

Kalit so‘zlar

bot detection, machine learning, user behavior analysis, ONNX model, real-time analysis, CAPTCHA alternatives., bot aniqlash, mashinaviy o‘rganish, foydalanuvchi xatti-harakatlari, ONNX modeli, real vaqtda tahlil, CAPTCHA alternativlari

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