Detecting and characterizing web bot traffic in a large e-commerce marketplace

Haitao Xu, Zhao Li, Chen Chu, Yuanmi Chen, Yifan Yang, Haifeng Lu, Haining Wang, Angelos Stavrou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

A certain amount of web traffic is attributed to web bots on the Internet. Web bot traffic has raised serious concerns among website operators, because they usually consume considerable resources at web servers, resulting in high workloads and longer response time, while not bringing in any profit. Even worse, the content of the pages it crawled might later be used for other fraudulent activities. Thus, it is important to detect web bot traffic and characterize it. In this paper, we first propose an efficient approach to detect web bot traffic in a large e-commerce marketplace and then perform an in-depth analysis on the characteristics of web bot traffic. Specifically, our proposed bot detection approach consists of the following modules: (1) an Expectation Maximization (EM)-based feature selection method to extract the most distinguishable features, (2) a gradient based decision tree to calculate the likelihood of being a bot IP, and (3) a threshold estimation mechanism aiming to recover a reasonable amount of non-bot traffic flow. The detection approach has been applied on Taobao/Tmall platforms, and its detection capability has been demonstrated by identifying a considerable amount of web bot traffic. Based on data samples of traffic originating from web bots and normal users, we conduct a comparative analysis to uncover the behavioral patterns of web bots different from normal users. The analysis results reveal their differences in terms of active time, search queries, item and store preferences, and many other aspects. These findings provide new insights for public websites to further improve web bot traffic detection for protecting valuable web contents.

Original languageEnglish (US)
Title of host publicationComputer Security - 23rd European Symposium on Research in Computer Security, ESORICS 2018, Proceedings
EditorsJianying Zhou, Miguel Soriano, Javier Lopez
PublisherSpringer Verlag
Pages143-163
Number of pages21
ISBN (Print)9783319989884
DOIs
StatePublished - Jan 1 2018
Event23rd European Symposium on Research in Computer Security, ESORICS 2018 - Barcelona, Spain
Duration: Sep 3 2018Sep 7 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11099 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd European Symposium on Research in Computer Security, ESORICS 2018
CountrySpain
CityBarcelona
Period9/3/189/7/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Xu, H., Li, Z., Chu, C., Chen, Y., Yang, Y., Lu, H., Wang, H., & Stavrou, A. (2018). Detecting and characterizing web bot traffic in a large e-commerce marketplace. In J. Zhou, M. Soriano, & J. Lopez (Eds.), Computer Security - 23rd European Symposium on Research in Computer Security, ESORICS 2018, Proceedings (pp. 143-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11099 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-98989-1_8