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

Continuous authentication in high-stakes digital environments requires datasets with fine-grained behavioural 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 (Behavioural Engine for Authentication & Continuous Monitoring), a large-scale multimodal dataset that captures diverse skill tiers in competitive Valorant gameplay. BEACON contains about 550 GB of synchronised telemetry data from 124 sessions, an estimated 160.69 hours of data, including high-frequency mouse dynamics, keystroke events, network packet captures, screen recordings, hardware metadata, and in-game configuration context. BEACON leverages the high precision motor skills and high cognitive load that are inherent to tactical shooters, making it a rigorous stress test for the robustness of behavioural biometrics. The dataset allows for the study of continuous authentication, behavioral profiling, user drift, and multimodal representation learning in a high-fidelity esports setting. The authors release the dataset and code on Hugging Face and GitHub to create a reproducible benchmark for evaluating next-generation behavioral fingerprinting and security models.