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 \textit{Valorant} gameplay. BEACON contains approximately 430 GB of synchronised modality data (461 GB total on-disk including auxiliary \textit{Valorant} configuration captures) from 79 sessions across 28 distinct players, estimated at 102.51 hours of active gameplay, 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 behavioral 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

翻译：在高风险数字环境中进行持续身份认证，需要能在真实认知和运动负荷下捕获细粒度行为信号的公开数据集。然而，现有基准通常受限于小规模、单模态感知或缺乏同步的环境上下文。为填补这一空缺，本文提出BEACON（行为引擎用于身份认证与持续监控），一个大规模多模态数据集，捕获了竞技游戏《无畏契约》中不同技术水平层次的行为。BEACON包含约430GB的同步模态数据（磁盘总容量461GB，含辅助的《无畏契约》配置采集），来自28名不同玩家的79次会话，估算为102.51小时的有效游戏时长，涵盖高频鼠标动态、击键事件、网络数据包捕获、屏幕录制、硬件元数据及游戏内配置上下文。BEACON利用了战术射击游戏固有的高精度运动技能和高认知负荷，为行为生物特征的鲁棒性提供了严格的压力测试。该数据集支持在高保真电竞环境下研究持续身份认证、行为画像、用户漂移及多模态表征学习。作者在Hugging Face和GitHub上发布数据集与代码，旨在创建可复现的基准，用于评估下一代行为指纹和安全性模型。