通过分析用户浏览行为区分真假用户，准确率达98%以上。
BOTracle: A framework for Discriminating Bots and Humans
- 仅依赖浏览行为，不使用IP、浏览器等静态信息
- 在4000万次真实电商访问中实现98%以上准确率
- 适合高流量场景下防爬虫与反作弊应用
机器人占互联网流量的很大比例，且在多个领域引发问题。现代机器人常模仿真实用户行为，包括使用真实浏览器，使得识别变得困难。本文针对高流量场景下的机器人检测挑战，评估了三种不同方法：第一种基于启发式规则，可快速识别；第二种利用常见技术特征（如IP地址、窗口大小、用户代理），作为对比基准；第三种则完全依赖浏览行为，摒弃所有静态特征，专注分析用户在网站上的动态操作。与已有研究不同，本工作基于包含4000万次月度页面访问的真实电商流量数据进行评估，并与另一检测方法Botcha在同一数据集上对比。实验结果表明，本方法在精确率、召回率和AUC三项指标上均达到98%或更高，优于Botcha。
原文摘要 · Abstract (English)
Bots constitute a significant portion of Internet traffic and are a source of various issues across multiple domains. Modern bots often become indistinguishable from real users, as they employ similar methods to browse the web, including using real browsers. We address the challenge of bot detection in high-traffic scenarios by analyzing three distinct detection methods. The first method operates on heuristics, allowing for rapid detection. The second method utilizes, well known, technical features, such as IP address, window size, and user agent. It serves primarily for comparison with the third method. In the third method, we rely solely on browsing behavior, omitting all static features and focusing exclusively on how clients behave on a website. In contrast to related work, we evaluate our approaches using real-world e-commerce traffic data, comprising 40 million monthly page visits. We further compare our methods against another bot detection approach, Botcha, on the same dataset. Our performance metrics, including precision, recall, and AUC, reach 98 percent or higher, surpassing Botcha.
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