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BotGraph: Web Bot Detection Based on Sitemap

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arxiv 1903.08074 v2 pith:AYOV2AM3 submitted 2019-03-19 cs.CR

classification cs.CR
keywords detectionbotgraphbotsclouddatasitemapsitesachieves
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The web bots have been blamed for consuming large amount of Internet traffic and undermining the interest of the scraped sites for years. Traditional bot detection studies focus mainly on signature-based solution, but advanced bots usually forge their identities to bypass such detection. With increasing cloud migration, cloud providers provide new opportunities for an effective bot detection based on big data to solve this issue. In this paper, we present a behavior-based bot detection scheme called BotGraph that combines sitemap and convolutional neural network (CNN) to detect inner behavior of bots. Experimental results show that BotGraph achieves ~95% recall and precision on 35-day production data traces from different customers including the Bing search engine and several sites.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BOTracle: A framework for Discriminating Bots and Humans

    cs.LG 2024-12 reject novelty 4.0 of 10

    BOTracle's multi-stage bot detection pipeline (heuristics, SGAN, DGCNN on traversal graphs) reaches high accuracy on one proprietary e-commerce dataset, yet its claim of beating Botcha is contradicted by its own Table 3.

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