Summary of Botracle: a Framework For Discriminating Bots and Humans, by Jan Kadel et al.
BOTracle: A framework for Discriminating Bots and Humans
by Jan Kadel, August See, Ritwik Sinha, Mathias Fischer
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes three distinct methods for detecting bots in high-traffic scenarios. The first method uses heuristics to rapidly detect bots, while the second method relies on technical features such as IP address, window size, and user agent. The third method focuses solely on browsing behavior, omitting static features and analyzing how clients interact with websites. The authors evaluate their approaches using real-world e-commerce traffic data and compare them to another bot detection approach, Botcha. The results show that the proposed methods achieve high precision, recall, and AUC scores of 98 percent or higher, outperforming Botcha. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bots are a big problem on the internet because they can cause trouble in many areas. To solve this problem, researchers have come up with three new ways to detect bots. The first way uses simple rules to quickly identify bots. The second way looks at technical details like where the bot is coming from and how it’s behaving. The third way only looks at what the bot does on a website. The scientists tested their methods using real data from online shopping websites and compared them to another popular method, Botcha. Their results show that these new methods are really good at detecting bots and can even do better than Botcha. |
Keywords
» Artificial intelligence » Auc » Precision » Recall
