WWW 2024, Resource track ACM Web Conference, 13-17 May 2024, Singapore, Singapore
Securing information systems is paramount across industries. Sound understanding of the user and equipment behavior is key for managing security risks and comprehending user activities’ impact on the network infrastructure. However, accessing network traffic
and Web logs is challenging due to encryption or decentralized systems. Qualifying activities also requires contextualizing them according to the network’s topology, as it determines potential exchanges and carries information about which services are used.
This complexity hinders learning behavioral patterns when precise user action sequences are needed. In this paper, we propose to tackle these challenges with Graphameleon, an open-source Web extension for capturing Web navigation traces. We model user activities
in an RDF knowledge graph, drawing from the UCO and NORIA-O ontologies. With this approach, we are able to distinguish analytics strategies implemented across different websites. Additionally, we successfully identified an online purchasing activity
pattern on a simulated website, albeit with constraints regarding minor variations in interactions like an XSS exploit.
Type:
Conférence
City:
Singapore
Date:
2024-05-13
Department:
Data Science
Eurecom Ref:
7478
Copyright:
© ACM, 2024. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in WWW 2024, Resource track ACM Web Conference, 13-17 May 2024, Singapore, Singapore
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