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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Accès en ligne: | https://arxiv.org/abs/2412.02266 |
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| _version_ | 1866915045964775424 |
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| author | Kadel, Jan See, August Sinha, Ritwik Fischer, Mathias |
| author_facet | Kadel, Jan See, August Sinha, Ritwik Fischer, Mathias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02266 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BOTracle: A framework for Discriminating Bots and Humans Kadel, Jan See, August Sinha, Ritwik Fischer, Mathias Machine Learning I.2; I.5; D.2 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. |
| title | BOTracle: A framework for Discriminating Bots and Humans |
| topic | Machine Learning I.2; I.5; D.2 |
| url | https://arxiv.org/abs/2412.02266 |