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 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，持续身份验证需要数据集在真实认知与运动负荷条件下提供精细粒度行为信号。然而，现有基准测试常受限于小规模、单模态感知或缺乏同步环境上下文。为弥合这一缺口，本文提出BEACON（行为引擎用于身份验证与持续监控），一个大规模多模态数据集，可捕捉《无畏契约》竞技游戏中的多样性技能层级。BEACON包含来自28位不同玩家的79场会话中约430 GB的同步模态数据（含辅助配置捕获的磁盘总容量为461 GB），估计对应102.51小时活跃游戏过程，涵盖高频鼠标动态、按键事件、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。该数据集利用战术射击游戏固有的高精度运动技能与高认知负荷，为行为生物特征的鲁棒性提供了严苛压力测试。数据集支持在高保真电竞场景中研究持续身份验证、行为轮廓分析、用户漂移效应及多模态表征学习。作者在Hugging Face和GitHub上发布数据集与代码，旨在为下一代行为指纹识别与安全模型评估构建可复现基准。