Scholarlink

Traditional CAPTCHAs often hinder users more than they stop bots. This project proposes a passive, user-friendly alternative that monitors behavior—like mouse movement, typing speed, and clicks—to distinguish humans from bots. Built with Python, FastAPI, MongoDB, and XGBoost, the system defends against threats like DoS/DDoS attacks while remaining seamless. It adapts over time through model updates and achieved 95.3% accuracy with minimal false positives. With response times under a second, it outperforms conventional CAPTCHAs in both speed and usability.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI Driven CAPTCHA-Based Security Alert for Identification and Preventing Malacious Bots

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Kaushal Kotkar,
  • Samiran Deore,
  • Riddhi Tak,
  • Tejal Deshmukh,
  • Rupali Vairagade,
  • Nilakshi Jain

摘要

Traditional CAPTCHAs often hinder users more than they stop bots. This project proposes a passive, user-friendly alternative that monitors behavior—like mouse movement, typing speed, and clicks—to distinguish humans from bots. Built with Python, FastAPI, MongoDB, and XGBoost, the system defends against threats like DoS/DDoS attacks while remaining seamless. It adapts over time through model updates and achieved 95.3% accuracy with minimal false positives. With response times under a second, it outperforms conventional CAPTCHAs in both speed and usability.