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KEP: Keystroke Evoked Potential for EEG-Based User Authentication

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Artificial Intelligence Security and Privacy (AIS&P 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14509))

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Abstract

In recent years, the rapid proliferation of Brain-Computer Interface (BCI) applications has made the issue of security increasingly important. User authentication serves as the cornerstone of any secure BCI systems, and among various methods, EEG-based authentication is particularly well-suited for BCIs. However, existing paradigms, such as visual evoked potentials and motor imagery, demand significant user efforts during both enrollment and authentication phases. To address these challenges, we introduce a novel paradigm–Keystroke Evoked Potentials (KEP) for EEG-based authentication, which is secure, user-friendly, and lightweight. Then, we design an authentication system based on our proposed KEP. The core concept involves generating a shared cryptographic session key derived from EEG data and keystroke dynamics captured during random button-pressing activities. This shared key is subsequently employed in a Diffie-Hellman Encrypted Key Exchange (DH-EKE) to facilitate device pairing and establish a secure communication channel. Based on a collected dataset, the results demonstrate that our system is secure against various attacks (e.g., mimicry attack, replay attack) and efficient in practice (e.g., taking only 0.07 s to generate 1 bit).

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Authors and Affiliations

  1. SPTAGE Lab, DTU Compute, Technical University of Denmark, Kongens Lyngby, 2800, Denmark

    Jiaxuan Wu, Wei-Yang Chiu & Weizhi Meng

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  1. Jiaxuan Wu
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  2. Wei-Yang Chiu
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Correspondence to Weizhi Meng .

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Editors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

  2. Tampere University, Tampere, Finland

    Moncef Gabbouj

  3. Guangzhou University, Guangzhou, China

    Jin Li

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Wu, J., Chiu, WY., Meng, W. (2024). KEP: Keystroke Evoked Potential for EEG-Based User Authentication. In: Vaidya, J., Gabbouj, M., Li, J. (eds) Artificial Intelligence Security and Privacy. AIS&P 2023. Lecture Notes in Computer Science, vol 14509. Springer, Singapore. https://doi.org/10.1007/978-981-99-9785-5_36

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  • DOI: https://doi.org/10.1007/978-981-99-9785-5_36

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