# BOTracle: A framework for Discriminating Bots and Humans

arXiv: https://arxiv.org/abs/2412.02266 · HTML: https://mlsanity.com/paper/2412.02266/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 50 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Taming the Cross Entropy Loss](https://mlsanity.com/paper/1810.05075/) (2018)
- [A Low-Cost Attack against the hCaptcha System](https://mlsanity.com/paper/2104.04683/) (2021)
- [BotGraph: Web Bot Detection Based on Sitemap](https://mlsanity.com/paper/1903.08074/) (2019)
- [Botcha: Detecting Malicious Non-Human Traffic in the Wild](https://mlsanity.com/paper/2103.01428/) (2021)

## Abstract (arXiv)

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.