Sign inGet started
Paper · arXiv

BEACON: A Multimodal Dataset for Learning Behavioral Fingerprints from Gameplay Data

Continuous authentication in high-stakes digital environments requires datasets with fine-grained behavioral signals under realistic cognitive and motor demands. But current benchmarks are often limited by small scale, unimodal sensing or lack of synchronised environmental context. To address this gap, this paper introduces BEACON (Behavioral Engine for Authentication & Continuous Monitoring), a large-scale multimodal dataset that captures diverse skill tiers in competitive Valorant gameplay. BEACON contains approximately 430 GB of synchronised modality data (461 GB total on-disk including auxiliary Valorant configuration captures) from 79 sessions across 28 distinct players, estimated at...

AuthorsIshpuneet Singh, Gursmeep Kaur, Uday Pratap Singh Atwal et al.
Primary categorycs.CR
Published11 May 2026
Last updated at source15 May 2026
cs.CRcs.AIcs.CVcs.LGcs.NI
Embed this entry
<iframe src="https://silytics.si/embed/item/ee849c6c-feec-4fff-99cc-c0278730f435" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Connected in the knowledge graph

Explore this entry in the graph

Models and datasets referencing this paper

Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.