基于多种策略与深度学习的反爬虫机制

Deep Learning-Based Multi-Strategy Anti-Crawler Mechanism

摘要: 目前针对网络爬虫的防御机制很多，传统的机制有基于验证码的机制，有基于抓取频率的，还有基于浏览器策略的机制，这些策略通常不能有效解决爬虫问题。在进行了大量的技术调研的基础上，本文提出了一种多策略的反爬虫机制，同时结合深度学习技术，有效提升了系统的反爬效果，进而构建了一个集token技术，前端技术、验证码技术、蜜罐技术、深度学习技术、规则库、黑白名单技术于一体反爬虫系统，该系统通有效地解决了爬虫问题。

Abstract: A variety of defenses against web crawlers have been proposed. Traditional approaches rely on CAPTCHAs, request-frequency limits, or browser-policy enforcement, yet they seldom resolve the crawler problem. Guided by an extensive technical survey, this paper presents a multi-strategy anti-crawler mechanism that integrates deep-learning techniques to markedly improve protection. The resulting system unifies token validation, front-end hardening, CAPTCHA challenges, honeypots, deep-learning analysis, and dynamic black & white-list. Online evaluations demonstrate that the system effectively mitigates crawler abuse.