Hybrid Feature Selection Method for Replay Attack Detection on Lightweight Devices 


Vol. 14,  No. 5, pp. 320-331, May  2025
https://doi.org/10.3745/TKIPS.2025.14.5.320


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  Abstract

As wearable devices with voice assistants, like smartwatches, become more common, the threat of replay attacks—where recorded voice commands are replayed to bypass authentication—continues to grow, posing serious risks to user security. While lightweight detection models have been proposed, their real-world applicability is limited due to the computational burden and redundancy of high-dimensional features. This study presents a lightweight detection framework using a reinforcement learning-based feature selection method that automatically selects only the most relevant features. By combining SHAP, Permutation Importance, and reinforcement learning, a reliable Top-K feature set is built to enhance both explainability and generalizability. The framework is broadly applicable across feature sets and adaptable to various voice-based environments. Experiments show that it maintains high detection performance with fewer features while reducing computational cost and improving training efficiency.

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[IEEE Style]

G. Jin and S. Lee, "Hybrid Feature Selection Method for Replay Attack Detection on Lightweight Devices," The Transactions of the Korea Information Processing Society, vol. 14, no. 5, pp. 320-331, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.5.320.

[ACM Style]

Gyujeong Jin and Seyoung Lee. 2025. Hybrid Feature Selection Method for Replay Attack Detection on Lightweight Devices. The Transactions of the Korea Information Processing Society, 14, 5, (2025), 320-331. DOI: https://doi.org/10.3745/TKIPS.2025.14.5.320.

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