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arxiv: 2605.10867 · v2 · pith:PRNR4GCYnew · submitted 2026-05-11 · 💻 cs.CR · cs.AI· cs.CV· cs.LG· cs.NI

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

Ishpuneet Singh , Gursmeep Kaur , Uday Pratap Singh Atwal , Guramrit Singh , Gurjot Singh , Maninder Singh This is my paper

Pith reviewed 2026-05-19 17:11 UTC · model grok-4.3

classification 💻 cs.CR cs.AIcs.CVcs.LGcs.NI
keywords behavioral biometricscontinuous authenticationmultimodal datasetgameplay dataesportssecuritybehavioral fingerprintsuser authentication
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The pith

A new multimodal dataset from competitive gameplay provides synchronized behavioral signals for testing continuous authentication systems.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents BEACON, a dataset comprising roughly 430 GB of data from 79 sessions of 28 players engaged in tactical shooter gameplay, totaling over 102 hours of play. It includes high-frequency mouse movements, keystrokes, network captures, screen recordings, hardware info, and game settings, all synchronized. This setup uses the demanding nature of such gameplay to challenge behavioral biometric methods. A sympathetic reader would care because it offers a large, realistic benchmark to improve security in digital environments where identity verification must happen continuously. The release of the data and code aims to standardize evaluation of models that learn user fingerprints from such rich signals.

Core claim

The authors establish that BEACON captures diverse skill levels in esports with fine-grained, synchronized multimodal data under high cognitive and motor demands, enabling studies of continuous authentication, behavioral profiling, user drift, and multimodal learning in a realistic setting.

What carries the argument

The BEACON dataset, which synchronizes multiple data modalities from gameplay to capture detailed behavioral patterns.

If this is right

  • The dataset supports development of continuous authentication methods that operate during intense activities.
  • It facilitates research on how user behavior drifts over time in high-stakes scenarios.
  • Multimodal representation learning can be advanced using the synchronized signals.
  • Security models can be evaluated against this benchmark for robustness in realistic conditions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Connecting this to broader security, the data might help distinguish legitimate players from impostors or bots in online games.
  • A testable extension would be to train classifiers on subsets of modalities to determine which signals contribute most to accurate identification.
  • Researchers could compare performance here against existing smaller datasets to quantify the benefit of scale and multimodality.

Load-bearing premise

The high precision motor skills and high cognitive load in tactical shooters create conditions that rigorously test the robustness of behavioral biometrics.

What would settle it

If authentication models show no improvement in accuracy or robustness when trained and tested on this dataset compared to simpler, low-demand datasets, the value as a stress test would be undermined.

Figures

Figures reproduced from arXiv: 2605.10867 by Guramrit Singh, Gurjot Singh, Gursmeep Kaur, Ishpuneet Singh, Maninder Singh, Uday Pratap Singh Atwal.

Figure 1
Figure 1. Figure 1: Architecture of the custom BEACON logger [ [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Log-scale distribution of the overall event and packet counts across the primary modalities, [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Aggregate heatmaps illustrating the spatial distribution of sensorimotor interactions across [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Cross-modality behavioral comparison. The Z-score heatmap standardises median features [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Inter-user separability analysis. The distribution of sensorimotor features for a single [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The secure data pipeline and upload portal, illustrating local caching, chunked HTTPS [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Statistical distribution of Keyboard dynamics across all 28 participants. Notice the high [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Statistical distribution of Mouse dynamics across all 28 participants. Features such as Event [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Statistical distribution of Network telemetry across all 28 participants. The integration of [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Detailed Performance Metrics for Unimodal Mouse Dynamics. [PITH_FULL_IMAGE:figures/full_fig_p023_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Detailed Performance Metrics for Unimodal Keyboard Dynamics. [PITH_FULL_IMAGE:figures/full_fig_p024_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Detailed Performance Metrics for Multi-modal Early Fusion (Combined Profile). [PITH_FULL_IMAGE:figures/full_fig_p025_12.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper introduces BEACON, a multimodal dataset for behavioral biometrics research consisting of approximately 430 GB (461 GB on-disk) of synchronized data from 79 sessions across 28 players in Valorant gameplay, totaling an estimated 102.51 hours. Modalities include high-frequency mouse dynamics, keystroke events, network packet captures, screen recordings, hardware metadata, and in-game configuration context. The work positions the dataset as a public benchmark for continuous authentication, behavioral profiling, user drift, and multimodal representation learning under high cognitive and motor demands, with releases on Hugging Face and GitHub.

Significance. If the collection protocols, synchronization, and released artifacts match the description, the dataset would provide a valuable large-scale resource for behavioral fingerprinting in esports settings, addressing gaps in existing benchmarks regarding scale, multimodality, and realistic stress conditions for authentication models.

major comments (1)
  1. [Abstract] Abstract and data description sections: the central claim of a synchronized multimodal dataset rests on unshown evidence regarding collection protocols, inter-modality synchronization validation (e.g., alignment of mouse events with screen recordings and network captures), and quality assurance steps; without these, the utility for reproducible behavioral biometrics research cannot be fully assessed.
minor comments (1)
  1. [Abstract] Clarify whether the 102.51 hours figure is computed from active gameplay logs or total session duration, and provide the exact method used for this estimation.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential value of the BEACON dataset. We address the major comment regarding documentation of collection protocols, synchronization validation, and quality assurance below.

read point-by-point responses

  1. Referee: [Abstract] Abstract and data description sections: the central claim of a synchronized multimodal dataset rests on unshown evidence regarding collection protocols, inter-modality synchronization validation (e.g., alignment of mouse events with screen recordings and network captures), and quality assurance steps; without these, the utility for reproducible behavioral biometrics research cannot be fully assessed.

    Authors: We agree that explicit details on collection protocols, inter-modality synchronization, and quality assurance are essential for reproducibility and that the current manuscript would benefit from greater elaboration in these areas. In the revised version, we will expand the Methods and Data Collection sections to include: (1) a step-by-step description of the hardware and software setup for each modality; (2) the synchronization mechanism, including use of a shared high-resolution system clock, NTP-based timestamping, and post-capture alignment validation via cross-correlation of event timestamps with screen recording frames and network packet arrival times; (3) quantitative validation results (e.g., measured alignment error bounds and sample checks); and (4) quality assurance procedures such as checksum verification, manual review of a subset of sessions, and automated detection of recording artifacts. These additions will directly support the synchronization claims made in the abstract and dataset description. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The manuscript is a dataset release paper whose central contribution is the description and public release of ~430 GB of synchronized multimodal gameplay recordings from Valorant sessions. It contains no equations, derivations, fitted parameters, predictions, or uniqueness theorems. All claims are descriptive (data volume, modalities captured, session counts, total hours) or motivational framing (positioning the data as a stress test for behavioral biometrics). No load-bearing step reduces to a self-citation, ansatz, or input by construction; the work is self-contained as an empirical data artifact with no internal derivation chain to inspect.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This is a dataset release paper containing no mathematical derivations, fitted parameters, or postulated entities; the only assumptions are standard domain practices for multimodal data capture and synchronization.

axioms (1)
  • domain assumption Multimodal signals can be accurately synchronized during high-speed gameplay capture
    Invoked implicitly when claiming synchronized data across mouse, network, and screen modalities.

pith-pipeline@v0.9.0 · 5777 in / 1287 out tokens · 55648 ms · 2026-05-19T17:11:20.103897+00:00 · methodology

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