CARONTE: crawling adversarial resources over non-trusted, high-profile environments

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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Samenvatting

The monitoring of underground criminal activities is often automated to maximize the data collection and to train ML models to automatically adapt data collection tools to different communities. On the other hand, sophisticated adversaries may adopt crawling-detection capabilities that may significantly jeopardize researchers' opportunities to perform the data collection, for example by putting their accounts under the spotlight and being expelled from the community. This is particularly undesirable in prominent and high-profile criminal communities where entry costs are significant (either monetarily or for example for background checking or other trust-building mechanisms). This paper presents CARONTE, a tool to semi-automatically learn virtually any forum structure for parsing and data-extraction, while maintaining a low profile for the data collection and avoiding the requirement of collecting massive datasets to maintain tool scalability. We showcase the tool against four underground forums, and compare the network traffic it generates (as seen from the adversary's position, i.e. the underground community’s server) against state-of-the-art tools for web-crawling as well as human users.
Originele taal-2Engels
TitelProceedings - 4th IEEE European Symposium on Security and Privacy Workshops, EUROS and PW 2019
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's433-442
Aantal pagina's10
ISBN van elektronische versie9781728130262
DOI's
StatusGepubliceerd - 1 jun 2019
Evenement4th IEEE European Symposium on Security and Privacy Workshops, EUROS and PW 2019 - Stockholm, Zweden
Duur: 17 jun 201919 jun 2019

Congres

Congres4th IEEE European Symposium on Security and Privacy Workshops, EUROS and PW 2019
Land/RegioZweden
StadStockholm
Periode17/06/1919/06/19

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